Evidence map›Paper›PMID 41726488›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Prospective Validation of a Suicide Event Risk Model in Transgender Patients.

Robert A Becker, Colin G Walsh

Abstract readValidation Study
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Robert A BeckerVanderbilt University Medical Center, Nashville, Tennessee, USA.
Colin G WalshVanderbilt University Medical Center, Nashville, Tennessee, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Suicide risk prediction models offer a promising avenue for early intervention, but their effectiveness in underrepresented populations remain uncertain. This study evaluated VSAIL's, a real-world, externally validated, and deployed suicide risk prediction model, performance in predicting suicide risk among transgender individuals. Transgender individuals were identified from electronic health record data and transgender status was verified via manual chart review. Results indicated modest discriminative ability (AUROC=0.777, AUPRC=0.115), however, a high rate of false negatives (77%), and significant miscalibration (Brier=0.023, Spiegelhalter's z-statistic p<0.001) reduced clinical utility. Findings underscore the importance of targeted subgroup validation and highlight limitations of general population-trained models in accurately identifying suicide risk among transgender patients. They also suggest the need for ongoing algorithm monitoring and subgroup-aware modeling strategies to improve predictive equity in marginalized populations.

Indexed as

SuicideTransgender PersonsAdultFemaleHumansMalePrediction AlgorithmsPredictive Learning ModelsProspective StudiesRisk Assessment

Identifiers

PMID41726488
PMCPMC12919586

What OpenQuestion holds

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Registered trials

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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.