Evidence map›Paper›PMID 40992914›Full record

ArticleWorld journal of surgery2025

Random Forest Machine Learning Matches Human Expert Accuracy in Trauma Severity Scoring.

G L Laing, J L Bruce, W Bekker, V Manchev, H Wain, D L Clarke

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of surgery, 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. 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

6 authors.

G L LaingDepartment of Surgery, University of KwaZulu-Natal, Durban, South Africa.ORCID https://orcid.org/0000-0001-8075-0386
J L BruceDepartment of Surgery, University of KwaZulu-Natal, Durban, South Africa.
W BekkerDepartment of Surgery, University of KwaZulu-Natal, Durban, South Africa.
V ManchevDepartment of Surgery, University of KwaZulu-Natal, Durban, South Africa.
H WainDepartment of Surgery, University of KwaZulu-Natal, Durban, South Africa.ORCID https://orcid.org/0000-0002-6693-0062
D L ClarkeDepartment of Surgery, University of KwaZulu-Natal, Durban, South Africa.ORCID https://orcid.org/0000-0002-8467-1455

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS) are essential for trauma care and research, yet manual scoring often yields incomplete data due to omissions. The hybrid electronic medical registry (HEMR) is used by our Level 1 trauma service for recording AIS and ISS.

methodsWe analyzed 21,704 patients with trauma records from the HEMR. Four machine learning (ML) algorithms predicted missing AIS scores per body region, from which ISS was derived mathematically. Performance was evaluated using coefficient of determination (R

resultsRandom forest models achieved R

conclusionRandom forest ML algorithms accurately predict missing AIS and ISS scores, significantly improving trauma registry data completeness while maintaining clinical accuracy equivalent to human expert scoring.

Indexed as

Abbreviated Injury ScaleInjury Severity ScoreMachine LearningTrauma Severity IndicesWounds and InjuriesAdultAlgorithmsFemaleHumansMaleRandom ForestRegistriesSensitivity and SpecificityAbbreviated Injury Scaleelectronic medical recordsinjury severity scoremachine learningnatural language processingrandom foresttrauma scoring

Identifiers

PMID40992914
PMCPMC12582138

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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.