Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications | Paper Citation Record

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Unresolved cited work

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 1999

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Unresolved cited work

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Cheng, Y

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Cheng, Y

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This paper cites \ Selbst, A.D.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Selbst, A.D

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Chan, C

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Hazzab, A

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Ross, P

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This paper cites APACrefauthors \ 2018.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2018

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Dudík, M

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Leemann, T

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Crupi, R

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2017

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Unresolved cited work

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Guestrin, C

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Roth, A

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Hilton, T.L

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Unresolved cited work

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Chaparro, X.A.F

Reference 20

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Feuerriegel, S

Reference 21

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Comparing interpretability and explainability for feature selection

Reference 22

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This paper cites APACrefauthors \ 2002.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2002

Reference 23

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Unresolved cited work

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Un” Fair Machine Learning Algorithms “un

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Huang, Y

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Ahern, J

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Atasoy, H

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Unresolved cited work

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 1912

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Tiribelli, S

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 1989

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Savona, M

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Oyallon, E

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Martin, B.R

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Price, E

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Pfaff, E

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Neumark, D

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Marsden, E

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Mitchell, M

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Kondrich, A.A

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Cardon, D

Reference 42

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Sharda, R

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Mao, X

Reference 44

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Calders, T

Reference 45

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Meng, Q

Reference 46

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Zare, Z

Reference 47

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2022

Reference 48

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This paper cites , Stegmaier, P.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Stegmaier, P

Reference 49

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This paper cites , Gaur, M.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Gaur, M

Reference 50

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This paper cites , Gaur, M.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Gaur, M

Reference 51

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Behrend, T.S

Reference 52

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Torous, J

Reference 53

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This paper cites , Cheng, K.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Cheng, K

Reference 54

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Setiono, R

Reference 55

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Lamy, F

Reference 56

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Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Joshi, S

Reference 57

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This paper cites APACrefauthors \ 2021.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2021

Reference 58

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This paper cites \ Stahler, G.J.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Stahler, G.J

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.254340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d1346d82-d8aa-4637-aeba-5c2f2a045ff3 · outbound

This paper cites , Stahler, G.J.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Stahler, G.J

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.243481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 395fbb64-2063-4ad7-9cff-7e39347989ee · outbound

This paper cites APACrefauthors \ 2021.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2021

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.233416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 037916ca-eccf-4d16-b7ea-9dd9fa889509 · outbound

This paper cites APACrefauthors \ 2020.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2020

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.222986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 25e3c615-df77-4726-9b20-8e41e4bb1fcb · outbound

This paper cites APACrefauthors \ 2021.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2021

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.212679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b7e3278f-1e0d-4ae4-b78a-0717499760c6 · outbound

This paper cites \ Srinivasan, S.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Srinivasan, S

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.202175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 245e9d3e-76fb-4cf2-817f-b3bb8093d74e · outbound

This paper cites APACrefauthors \ 2014.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2014

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.192133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0dd97220-5dfb-4b9c-bc33-f4ca0f896b94 · outbound

This paper cites APACrefauthors \ 2019.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2019

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.181045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5ccaf05a-e399-464b-baa9-36aa929f5b87 · outbound

This paper cites , Powers, B.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Powers, B

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.168656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cece9ad2-1e68-45a6-a185-2a3197cd6a98 · outbound

This paper cites APACrefauthors \ 2011.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2011

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:29.167202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20bd04e3-165b-499d-8e82-ff3c23773e36 · outbound

This paper cites , Pansera, M.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Pansera, M

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.158218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dece39d0-54d7-4538-b70f-b6429d5600b9 · outbound

This paper cites \ Ellwood, P.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Ellwood, P

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.148510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8aeb34f1-bfc9-44a0-8e44-4ef438e36edd · outbound

This paper cites , Teeple, S.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Teeple, S

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.138886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4ba62986-2742-4a39-b37a-5d07885fcb2a · outbound

This paper cites \ Kent, D.M.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Kent, D.M

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.128792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ae4bcea6-003c-4b85-98f1-00826d3f15b4 · outbound

This paper cites Enough,”“Enough.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Enough,”“Enough

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.116963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c58d59da-1408-4b5d-9134-778b126b5364 · outbound

This paper cites APACrefauthors \ 2018.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2018

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.105436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ef263912-3561-4009-a181-f2d81a917e8f · outbound

This paper cites APACrefauthors \ 1971.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 1971

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.091847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cd7e79fa-b7f0-43c4-a78b-3b73ea2d71af · outbound

This paper cites APACrefauthors \ 2021.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2021

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.078226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e979f63e-21c4-434c-8fee-53ab326ae89c · outbound

This paper cites , Allan, S.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Allan, S

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.066260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bf258b70-3393-4574-b1f8-a98c38badae7 · outbound

This paper cites \ Choo, E.K.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Choo, E.K

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.054395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 23d7a3da-cdd5-484c-84c7-bd444a084f88 · outbound

This paper cites APACrefauthors \ 2010.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2010

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.041638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f70e6804-9571-4fe5-a532-45a55d4e9ca1 · outbound

This paper cites , Porumbescu, G.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Porumbescu, G

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.029985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9d400728-5ba1-4aad-9a27-b19defd88e3e · outbound

This paper cites , Inza, I.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Inza, I

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.018326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fa765c59-c0f6-4469-8f89-b468729a5edb · outbound

This paper cites APACrefauthors \ 2024.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2024

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:30.007952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f7ca8a7a-2c13-4e58-b473-3a449a8e14bc · outbound

This paper cites APACrefauthors \ 2021.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2021

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.996372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e36581a1-9808-4677-96f5-d9d64e2335fe · outbound

This paper cites APACrefauthors \ 2017.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2017

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.985317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 93b4791f-30b2-4b9a-963c-2dcb9dea2318 · outbound

This paper cites APACrefauthors \ 1979.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 1979

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.973286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ec20b14a-2a7e-460e-ba50-4e17e93957b1 · outbound

This paper cites , Calandra, D.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Calandra, D

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.961377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ca55ad5a-281f-413b-97e0-98632cbe94a4 · outbound

This paper cites , Rappon, T.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Rappon, T

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.951067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5a855d7c-a6b1-46e7-9a13-19936809d8c5 · outbound

This paper cites \ Park, Y.J.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Park, Y.J

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.940914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ab1b33c9-705b-495c-8d77-28344f135176 · outbound

This paper cites , Kursuncu, U.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Kursuncu, U

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.929410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 796c3c0d-2906-49cb-8e4f-bd3ab8d8011d · outbound

This paper cites , Neveditsin, N.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Neveditsin, N

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.917172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0fde6627-4f50-4cfa-b209-f06788e1a02b · outbound

This paper cites an unresolved cited work.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:21:29.906933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d5542b49-b16d-4715-9a81-8008fd121b52 · outbound

This paper cites , Tucker, A.L.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Tucker, A.L

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.895304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 58311b29-de8a-4a93-aec9-65f45a1fc4df · outbound

This paper cites APACrefauthors \ 2018.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2018

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.882475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e9f569b-1b0e-496b-9b7a-afe79813312d · outbound

This paper cites , Deruyck, P.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Deruyck, P

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.870911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c15709dc-3325-4be5-8c39-97819630c7c6 · outbound

This paper cites , Zwiggelaar, R.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications , Zwiggelaar, R

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.858596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 409c509b-ca4f-4ff7-aded-20e2408dca06 · outbound

This paper cites an unresolved cited work.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:21:29.847616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a244d3a6-8eb1-40be-85c7-8612694c601f · outbound

This paper cites APACrefauthors \ 1971.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 1971

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.835745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1a218fca-00c2-4cab-acdd-e95a9fe1cc01 · outbound

This paper cites APACrefauthors \ 1996.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 1996

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.824997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8197f500-ff03-4b84-9d76-08a8163bc09a · outbound

This paper cites \ Gonzalez-Sanz, J.D.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications \ Gonzalez-Sanz, J.D

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.814340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1a7b1464-3847-4da5-be8b-afa498871d93 · outbound

This paper cites APACrefauthors \ 2010.

Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications APACrefauthors \ 2010

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:29.803678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers