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Record W2103033082 · doi:10.1016/j.respe.2011.04.004

Adaptation au codage CIM-10 de 15 indicateurs de la sécurité des patients proposés par l’Agence étasunienne pour la recherche et la qualité des soins de santé (AHRQ)

2011· article· fr· W2103033082 on OpenAlexaff
Jean‐Marie Januel, Chantal Marie Couris, Jean‐Christophe Luthi, Patricia Halfon, Béatrice Trombert‐Paviot, Hude Quan, Saskia E. Drösler, Vijaya Sundararajan, Eric Pradat, Sandrine Touzet, Eugene Wen, Jennie Shepheard, Greg Webster, Patrick S. Romano, Lawrence So, Lori Moskal, Linda Tournay-Lewis, Lalitha Sundaresan, Ed Kelley, Niek Klazinga, William A. Ghali, Cyrille Colin, Bernard Burnand

Bibliographic record

VenueRevue d Épidémiologie et de Santé Publique · 2011
Typearticle
Languagefr
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsCanadian Institute for Health InformationUniversity of Calgary
Fundersnot available
KeywordsMedicineHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.011
metaresearch head score (Gemma)0.043
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: Methods · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.543
GPT teacher head0.512
Teacher spread0.031 · 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
GenreMethods

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

Citations5
Published2011
Admission routes1
Has abstractno

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