Paper Citation Record · LEDGER

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity

As of 11 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2412.07879.

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

pith.paper-citation-record.v1
2412.07879 v1

Coverage vector

measured 59 of 59 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-08-06T23:35:06.743131Z

Reference resolution

59 of 59 outbound references displayed

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External citation measurements

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Outbound references

Observation 586c0cfe-24e8-48b3-ab63-b347e43c1679 · outbound

This paper cites Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities

Reference 1

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Observation 0b04d06d-593e-4b0e-8cc2-e5154d56b7e6 · outbound

This paper cites Ensuring Fairness in Machine Learning to Advance Health Equity.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Ensuring Fairness in Machine Learning to Advance Health Equity

Reference 2

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This paper cites Implementing Machine Learning in Health Care — Addressing Ethical Challenges.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Implementing Machine Learning in Health Care — Addressing Ethical Challenges

Reference 3

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This paper cites Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle

Reference 4

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This paper cites Dissecting racial bias in an algorithm used to manage the health of populations.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Dissecting racial bias in an algorithm used to manage the health of populations

Reference 5

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This paper cites TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Reference 6

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This paper cites Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy

Reference 7

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Unresolved cited work

Reference 8

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This paper cites A Survey on Bias and Fairness in Machine Learning.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity A Survey on Bias and Fairness in Machine Learning

Reference 9

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity How We Analyzed the COMPAS Recidivism Algorithm

Reference 10

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Observation 7e4b3a61-df4f-41a5-a4b7-1434e6b663f2 · outbound

This paper cites Algorithmic Bias? An Empirical Study into Apparent Gender- Based Discrimination in the Display of STEM Career Ads.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Algorithmic Bias? An Empirical Study into Apparent Gender- Based Discrimination in the Display of STEM Career Ads

Reference 11

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Observation b9c660ba-ee34-4301-b84c-3b324b563711 · outbound

This paper cites Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products

Reference 12

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This paper cites AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

Reference 13

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This paper cites Fairness definitions explained.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Fairness definitions explained

Reference 14

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This paper cites The Unfairness of Fair Machine Learning: Levelling down and Strict Egalitarianism by Default.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity The Unfairness of Fair Machine Learning: Levelling down and Strict Egalitarianism by Default

Reference 15

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Observation 74c3d14d-792d-4538-9e0a-ac5e8ca374d2 · outbound

This paper cites Race Bias, Social Class Bias, and Gender Bias in Clinical Judgment.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Race Bias, Social Class Bias, and Gender Bias in Clinical Judgment

Reference 16

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This paper cites A Historical Overview of Health Disparities and the Potential of eHealth Solutions.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity A Historical Overview of Health Disparities and the Potential of eHealth Solutions

Reference 17

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This paper cites The Root Causes of Health Inequity.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity The Root Causes of Health Inequity

Reference 18

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This paper cites Who cares about equity in the NHS? BMJ.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Who cares about equity in the NHS? BMJ

Reference 19

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This paper cites Accessed March 1, 2024.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Accessed March 1, 2024

Reference 20

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity The meaning and goals of equity in health

Reference 21

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Health disparities and health equity: concepts and measurement

Reference 22

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity A glossary for health inequalities

Reference 23

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Decision Curve Analysis: A Novel Method for Evaluating Prediction Models

Reference 24

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Decision Making in Health and Medicine: Integrating Evidence and Values

Reference 25

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity From Utilitarian to Rawlsian Designs for Algorithmic Fairness

Reference 26

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Causal Conceptions of Fairness and their Consequences

Reference 27

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity The Leicester Risk Assessment score for detecting undiagnosed Type 2 diabetes and impaired glucose regulation for use in a multiethnic UK setting

Reference 28

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Observation 0e81cd42-d4fc-4a4b-ad5b-c3ee801309a5 · outbound

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Accessed March 14, 2024

Reference 29

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Diabetes Care

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Ethnicity and Type 2 diabetes in the UK

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Risk Prediction Model Versus United States Preventive Services Task Force Lung Cancer Screening Eligibility Criteria: Reducing Race Disparities

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Second round results from the Manchester ‘Lung Health Check’ community-based targeted lung cancer screening pilot

Reference 33

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This paper cites Implementing lung cancer screening: baseline results from a community-based ‘Lung Health Check’ pilot in deprived areas of Manchester.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Implementing lung cancer screening: baseline results from a community-based ‘Lung Health Check’ pilot in deprived areas of Manchester

Reference 34

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Systematic Review of Lung Cancer Screening: Advancements and Strategies for Implementation

Reference 35

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity mice: Multivariate Imputation by Chained Equations in R

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

Reference 37

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Improving predictive inference under covariate shift by weighting the log- likelihood function

Reference 38

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Transporting a Prediction Model for Use in a New Target Population

Reference 39

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness

Reference 40

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Evaluation of clinical prediction models (part 1): from development to external validation

Reference 41

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

Reference 42

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare

Reference 43

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Race Corrections in Clinical Algorithms Can Help Correct for Racial Disparities in Data Quality

Reference 44

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Assessing the net benefit of machine learning models in the presence of resource constraints

Reference 45

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Race and ethnicity – a part of the equation for personalized clinical decision making? Circ Cardiovasc Qual Outcomes

Reference 46

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Inequalities from Lorentz-Finsler norms

Reference 47

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This paper cites Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms

Reference 48

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity All else being equal, men and women are still not the same: using risk models to understand gender disparities in care

Reference 49

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity AI recognition of patient race in medical imaging: a modelling study

Reference 50

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Toward the elimination of race-based medicine: replace race with racism as preeclampsia risk factor

Reference 51

Resolution: verified exact; doi, observed 2026-08-11T18:34:18.681499Z

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Observation 518cfd90-1366-4b3b-b291-4586af89b822 · outbound

This paper cites A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes

Reference 52

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Unavailable: canonical work link unavailable.

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This paper cites The expected value of sample information calculations for external validation of risk prediction models.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity The expected value of sample information calculations for external validation of risk prediction models

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:34:18.661640Z

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Observation c780d3cc-75fd-4e19-a433-99daba407d73 · outbound

This paper cites A framework for digital health equity.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity A framework for digital health equity

Reference 55

Resolution: verified exact; doi, observed 2026-08-11T18:34:18.627252Z

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Observation b0eb9dc7-f01f-4993-aded-cde0b9c6972f · outbound

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Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-11T18:34:19.897701Z

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Observation 08b92e75-9ad3-49b5-89a9-6a80058b81c3 · outbound

This paper cites an unresolved cited work.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-11T18:34:19.886336Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4578a337-b88f-4382-9b32-8f3869c192b1 · outbound

This paper cites an unresolved cited work.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-11T18:34:19.874945Z

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Observation 95b1169e-7b6e-4eea-ac41-28e54067fe55 · outbound

This paper cites Validation metrics are calculated as usual, without weighting.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Validation metrics are calculated as usual, without weighting

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:34:19.860622Z

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Observation 751e1b2e-b65e-4c80-8683-5e715a05254b · outbound

This paper cites an unresolved cited work.

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity Unresolved cited work

Reference 938

Resolution: verified exact; doi, observed 2026-08-11T18:34:18.999917Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation d956c5f3-b42d-4e8c-b8d3-b9cb545969cb · inbound

Critical Appraisal of Fairness Metrics in Clinical Predictive AI cites this paper.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T23:35:06.747824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.