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Record W4412879352 · doi:10.1016/j.xjon.2025.07.003

Surgical gloves need a better fit: Promoting equity in operating rooms

2025· article· en· W4412879352 on OpenAlexaff
Hamnah Majeed, Emmanuel Moss, Haris Majeed, Jae Byun, Eslem Altın, Bobby Yanagawa

Bibliographic record

VenueJTCVS Open · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsSt. Michael's HospitalMcGill University Health CentreJewish General HospitalUniversity of Toronto
Fundersnot available
KeywordsSurgical GlovesEquity (law)Surgical proceduresBusinessMedicineSurgeryPolitical science

Abstract

fetched live from OpenAlex

The first surgical gloves were created by the Goodyear Rubber Company in 1889 to protect the hands of the female head scrub nurse of Dr William Halsted. Women generally have smaller hands compared with men, with the median glove size being 6.0 versus 7.5, respectively, among surgeons.1 Despite anatomic differences in male and female hand measurements, there exists a paucity of research regarding the fit of these gloves. Improper glove sizing could result in musculoskeletal pain, reduced fine motor performance, and dissatisfaction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0060.006
Open science0.0010.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.157
GPT teacher head0.559
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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