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Record W4280545254 · doi:10.46747/cfp.6805340

Going against the status quo in screening

2022· article· en· W4280545254 on OpenAlexafffundvenue
Viola Antao, Roland Grad, Guylène Thériault, James A. Dickinson, Olga Szafran, Harminder Singh, Raphael Rezkallah, Earle H. Waugh, Neil R. Bell

Bibliographic record

VenueCanadian Family Physician · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare ManitobaUniversity of CalgaryMcGill UniversityUniversity of AlbertaMcGill University Health CentreUniversity of Toronto
FundersWomen's College Hospital
KeywordsStatus quoHealth careMedicineComputer scienceData scienceMedical educationPolitical science

Abstract

fetched live from OpenAlex

Screening as part of preventive health care is complex, and teaching these skills poses specific challenges. Multiple factors influence how clinician teachers elect to address screening. Direct-to-patient provincial screening programs and limited clinical time and resources make it easy to adopt a

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)0.069
metaresearch head score (Gemma)0.121
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 score0.734
Threshold uncertainty score0.528

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.121
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0140.049
Scholarly communication0.0160.012
Open science0.0030.008
Research integrity0.0130.028
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 head0.055
GPT teacher head0.349
Teacher spread0.295 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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