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Record W3198739666 · doi:10.18192/uojm.v11is1.6015

Les couleurs de la médecine : Représentation inégale de la pigmentation de la peau dans les ressources en dermatologie

2021· article· fr· W3198739666 on OpenAlexaffvenue
Yasmine Elmi

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2021
Typearticle
Languagefr
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesArtMedicine

Abstract

fetched live from OpenAlex

Les patients à la peau foncée peuvent présenter des variantes morphologiques et des maladies moins apparentes nécessitant une prise en charge et des thérapies uniques [1]. Cependant, les ressources pédagogiques actuelles en dermatologie ne permettent pas aux médecins d'acquérir la base de connaissances nécessaire pour diagnostiquer et traiter les maladies de la peau chez les patients ethniques. Cela constitue un obstacle important au traitement équitable des patients et à la formation des médecins. Des informations visuelles et textuelles plus cohérentes décrivant les maladies de la peau chez les personnes de couleur devraient être largement intégrées dans les ressources pédagogiques.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score1.000
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.025
GPT teacher head0.357
Teacher spread0.332 · 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.

Study designObservational
DomainReporting
GenreEmpirical

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
Published2021
Admission routes2
Has abstractyes

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Same venueUniversity of Ottawa Journal of MedicineSame topicOral Health Pathology and TreatmentFrench-language works237,207