Paper Citation Record · LEDGER

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

As of 16 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:1908.09254.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.09254 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:19:35.608516Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:50:28.505875Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-14T04:50:28.592833Z

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy17
unresolved11
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 10b6ab4a-6742-4078-a41c-a238a39d4a19 · outbound

This paper cites The reality of neonatal pain.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos The reality of neonatal pain

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.390635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d3547b4f-3b0e-4ae2-ae45-f15eb59f57ee · outbound

This paper cites Current controversies regarding pain assessment in neonates,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Current controversies regarding pain assessment in neonates,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.372338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a6f15cd7-f814-4950-b5d7-9b36dfe6f3fd · outbound

This paper cites Validation of the pain assessment in neonates (pain) scale with the neonatal infant pain scale (nips),.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Validation of the pain assessment in neonates (pain) scale with the neonatal infant pain scale (nips),

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.461155Z

Source-reported events for the cited work

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Observation 0b93c4a2-04c4-4ab9-bdb9-eb8ba9c34458 · outbound

This paper cites Clinical relia- bility and validity of the n-pass: neonatal pain, agitation and sedation scale with prolonged pain,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Clinical relia- bility and validity of the n-pass: neonatal pain, agitation and sedation scale with prolonged pain,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.341949Z

Source-reported events for the cited work

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Observation d8ed90db-41c7-4c82-91c9-2f88eece2422 · outbound

This paper cites Pain expression in neonates: facial action and cry,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Pain expression in neonates: facial action and cry,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.319381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fcedecd5-7c77-4938-9921-f8a6a64655f8 · outbound

This paper cites Neonatal facial coding system for assessing postoperative pain in infants: item reduction is valid and feasible,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Neonatal facial coding system for assessing postoperative pain in infants: item reduction is valid and feasible,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.300194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 75579c54-e350-4e2d-b880-172191b5097e · outbound

This paper cites Infants’ pain recognition based on facial expression: Dynamic hybrid descriptions,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Infants’ pain recognition based on facial expression: Dynamic hybrid descriptions,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.282042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9da7ff4c-5dbf-4a7c-bdfd-353c2bcab4d0 · outbound

This paper cites Body movements: an important additional factor in discriminating pain from stress in preterm infants,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Body movements: an important additional factor in discriminating pain from stress in preterm infants,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.262978Z

Source-reported events for the cited work

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Observation 89da19ca-4849-41ab-9af7-cf5502547f28 · outbound

This paper cites Pain assessment in human fetus and infants,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Pain assessment in human fetus and infants,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.231707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fb12cf43-286b-4aef-88e2-85fac53a7f6d · outbound

This paper cites A review of automated pain assessment in infants: Features, classiﬁ- cation tasks, and databases,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos A review of automated pain assessment in infants: Features, classiﬁ- cation tasks, and databases,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.498533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6c8e38e-e966-4593-955a-549ca3013892 · outbound

This paper cites Introduction to neonatal facial pain detection using common and advanced face classiﬁcation techniques,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Introduction to neonatal facial pain detection using common and advanced face classiﬁcation techniques,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.195535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 259ce897-9dc5-41cd-9a93-96141173ce48 · outbound

This paper cites A local approach based on a local binary patterns variant texture descriptor for classifying pain states,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos A local approach based on a local binary patterns variant texture descriptor for classifying pain states,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.174418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f220acd2-48cd-47fb-a060-3a0574c9770f · outbound

This paper cites Relevance vector machine learning for neonate pain intensity assessment using digital imaging,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Relevance vector machine learning for neonate pain intensity assessment using digital imaging,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.151991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4716623a-e332-40be-8f16-1528f1660065 · outbound

This paper cites An approach for automated multimodal analysis of infants’ pain,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos An approach for automated multimodal analysis of infants’ pain,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.130187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd87a248-5a78-4a85-933c-470a1f6ff5d2 · outbound

This paper cites Deep pain: Exploiting long short- term memory networks for facial expression classiﬁcation,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Deep pain: Exploiting long short- term memory networks for facial expression classiﬁcation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.111623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3cce4efa-3d28-48dd-a02b-ec878b302817 · outbound

This paper cites Spatio-temporal pain recognition in cnn-based super-resolved facial images,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Spatio-temporal pain recognition in cnn-based super-resolved facial images,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.093074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fce0a4bb-e5bf-4093-8b16-e9565a2afefd · outbound

This paper cites Neonatal facial pain assessment combining hand-crafted and deep features,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Neonatal facial pain assessment combining hand-crafted and deep features,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.074800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dba763da-a5dd-4c05-9095-71c326bf9212 · outbound

This paper cites YOLOv3: An Incremental Improvement.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos YOLOv3: An Incremental Improvement

Reference 18

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Source-reported events for the cited work

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Observation 7e8f5a33-1fc5-4e82-a0df-7048cc9e7ea1 · outbound

This paper cites Wider face: A face detection benchmark,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Wider face: A face detection benchmark,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.056761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 79479ced-699c-4be5-b6fc-658ac3b5808c · outbound

This paper cites Microsoft coco: Common objects in context,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Microsoft coco: Common objects in context,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.555650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93562471-651f-423a-906c-99186a55e552 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.562594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee83676c-f0e1-4e06-b426-176847671ef1 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Imagenet classiﬁcation with deep convolutional neural networks,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.568464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a6eeb1e-b482-4851-ab7f-13854081570d · outbound

This paper cites Vg- gface2: A dataset for recognising faces across pose and age,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Vg- gface2: A dataset for recognising faces across pose and age,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:19:36.014742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1476c42c-9645-4577-abd8-a5900d04c5df · outbound

This paper cites Deep face recognition.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Deep face recognition

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.580650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ba958b1-3c32-4cb0-94cd-151603e8c6f9 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Imagenet: A large-scale hierarchical image database,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.585978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c59ab3ff-0869-4166-8790-73894cf68654 · outbound

This paper cites Long short-term memory,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Long short-term memory,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.591357Z

Source-reported events for the cited work

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Observation 866082e2-2fbd-43ee-91a7-0ae9e951cd57 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Adam: A Method for Stochastic Optimization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.597142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fb4f8b8-3d0c-49e2-9aa7-b071fbd60753 · outbound

This paper cites Convolutional neural networks for neonatal pain assessment,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos Convolutional neural networks for neonatal pain assessment,

Reference 28

Resolution: metadata mismatch; raw_fallback, observed 2026-08-14T11:19:35.907344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dae700db-0049-47e5-b953-21de50d2097e · outbound

This paper cites A comprehensive and context-sensitive neonatal pain as- sessment using computer vision,.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos A comprehensive and context-sensitive neonatal pain as- sessment using computer vision,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:35.608516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e6cc09b3-7b63-489e-a715-4cdf5e61e668 · inbound

Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound cites this paper.

Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:50:28.599367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.