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Record W4286511451 · doi:10.1002/rth2.12767

Underrepresentation and undertreatment of women in hematology: An unsolved issue

2022· article· en· W4286511451 on OpenAlexaff
Kiera Liblik, Arkadeep Dhali, Vincent Kipkorir, Chaithanya Avanthika, Muhammad Romail Manan, Mihnea‐Alexandru Găman

Bibliographic record

VenueResearch and Practice in Thrombosis and Haemostasis · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsQueen's University
Fundersnot available
KeywordsGender equityEquity (law)Health careGender gapHealth equityRepresentation (politics)MedicineClinical trialGender disparityGerontologyFamily medicinePsychologyDemographyPolitical scienceDemographic economicsNursingPublic healthSocial scienceInternal medicineSociology

Abstract

fetched live from OpenAlex

Gender disparity is pervasive and persisting in research. Despite gender being recognized as one of the primary determinants of health, inadequate representation of women in clinical trials has resulted in a deficit pertaining to equity in health care. This gross underrepresentation has exposed women to unforeseen health-related outcomes, and as evident through historic records, unequal distribution of opportunities has further widened this gender gap in health care.

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)0.030
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.072
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.297
GPT teacher head0.508
Teacher spread0.211 · 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 designNot applicable
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

Citations15
Published2022
Admission routes1
Has abstractyes

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