Mitigating algorithmic bias in opioid risk-score modeling to ensure equitable access to pain relief.

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

3 citing papers in PubMed.

1. Evaluating the validity and performance of a modifiable prediction model to predict new long-term opioid use in prescription drug monitoring programs.JAMIA open · 2026
   Article
2. Introduction to the Special Issue on Transforming Prevention Science: Advances and Applications of Artificial Intelligence and Related Methods.Prevention science : the official journal of the Society for Prevention Research · 2026
   Article
3. Algorithmic opacity in opioid risk scoring and the need for transparent AI regulation.NPJ digital medicine · 2026
   Article

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

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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