ArticleProceedings of machine learning research2023
Fair admission risk prediction with proportional multicalibration.
Article in Proceedings of machine learning research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06434220 (Effect of Predictive Model on ED Physician Assessments of Patient Disposition), which is not on this map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Effect of Predictive Model on ED Physician Assessments of Patient Disposition
Who cites it
4 citing papers in PubMed.
- Deciphering the influence of demographic factors on the treatment of pediatric patients in the emergency department.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Calibrated Variant Effect Prediction at the Residue Level Using Conditional Score Distributions.bioRxiv : the preprint server for biology · 2025Article
- From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR.Proceedings of machine learning research · 2024Article
- Differential Fairness: An Intersectional Framework for Fair AI.Entropy (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Fair calibration is a widely desirable fairness criteria in risk prediction contexts. One way to measure and achieve fair calibration is with multicalibration. Multicalibration constrains calibration error among flexibly-defined subpopulations while maintaining overall calibration. However, multicalibrated models can exhibit a higher percent calibration error among groups with lower base rates than groups with higher base rates. As a result, it is possible for a decision-maker to learn to trust or distrust model predictions for specific groups. To alleviate this, we propose
Identifiers
37576024PMC10417639What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.