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Record W2738357644 · doi:10.1016/j.jocd.2017.05.011

Tools for Enhancement and Quality Improvement of Peer Assessment and Clinical Care in Endocrinology and Metabolism

2017· review· en· W2738357644 on OpenAlexaff
Aliya Khan, Anne B. Kenshole, Shereen Ezzat, Jeannette Goguen, Karen Gomez-Hernandez, Robert A. Hegele, Robyn L. Houlden, Tisha Joy, D.W. Killinger, André Lacroix, Sheila Laredo, Ally P.H. Prebtani, Muhammad Shrayyef, Christopher Tran, Stan Van Uum, Rhoda Reardon, Antiope Papageorgiou, William J. Tays, Merrill Edmonds

Bibliographic record

VenueJournal of Clinical Densitometry · 2017
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsCollege of Physicians and Surgeons of OntarioWomen's College HospitalCentre Hospitalier de l’Université de MontréalUniversity of OttawaPrincess Margaret Cancer CentreKingston General HospitalMount Sinai HospitalUniversity of TorontoWestern UniversitySt. Michael's HospitalMcMaster UniversityToronto General HospitalQueen's UniversityOttawa HospitalMcMaster University Medical Centre
FundersJavna Agencija za Raziskovalno Dejavnost RS
KeywordsMedicineDocumentationPatient careMedical educationInternal medicineQuality (philosophy)MEDLINEPhysiologyNursingBiochemistryComputer science

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)0.015
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.004
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0030.003
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 head0.722
GPT teacher head0.751
Teacher spread0.029 · 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 designNot applicable
DomainEvaluation
GenreReview

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

Citations3
Published2017
Admission routes1
Has abstractno

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