Evidence map›Paper›PMID 40791577›Full record

ArticleForensic science international. Synergy2025

The problem with eliminations: Why forensic comparisons need false negative rates.

Maria Cuellar

Abstract read
In one paragraph

Article in Forensic science international. Synergy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Assessing the foundations of forensic identification evidence: A critical examination of proficiency test design and results.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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

1 author.

Maria CuellarUniversity of Pennsylvania, Department of Criminology and Department of Statistics and Data Science, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article examines the overlooked risk of false negative errors arising from eliminations in forensic firearm comparisons. While recent reforms in forensic science have focused on reducing false positives, eliminations - often based on class characteristics or intuitive judgments - receive little empirical scrutiny despite their potential to exclude true sources. In cases involving a closed pool of suspects, eliminations can function as de facto identifications, introducing serious risk of error. A review of existing validity studies reveals that many report only false positive rates, failing to provide a complete assessment of method accuracy. This asymmetry is reinforced by professional guidelines, such as those from AFTE, and echoed in major government reports, including those from NAS and PCAST. The article argues that eliminations, like identifications, must be validated through rigorous testing and reported with transparent error rates. It further cautions against the use of "common sense" eliminations in the absence of empirical support and highlights the dangers of contextual bias when examiners are aware of investigative constraints. Five policy recommendations are proposed to improve the scientific treatment and legal interpretation of eliminations, including balanced reporting of false positive and false negative rates, validation of intuitive judgments, and clear warnings against using eliminations to infer guilt in closed-pool scenarios. Without reform, eliminations will continue to escape scrutiny, perpetuating unmeasured error and undermining the integrity of forensic conclusions.

Indexed as

Black box studiesCategorical conclusionsComparisonContextual biasForensic scienceValidity studies

Identifiers

PMID40791577
PMCPMC12337876

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.