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Record W2964961558 · doi:10.1007/s11606-019-05196-z

Expert Recommendations on Frequency of Utilization of Common Laboratory Tests in Medical Inpatients: a Canadian Consensus Study

2019· article· en· W2964961558 on OpenAlexafffundabout
Anshula Ambasta, Stefana Pancic, Brian M. Wong, Todd A. Lee, Deirdre McCaughey, Irene Ma

Bibliographic record

VenueJournal of General Internal Medicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsMcGill UniversityCanadian Patient Safety InstituteUniversity of TorontoUniversity of Calgary
FundersAlberta Health Services
KeywordsMedicineMedical laboratoryDelphi methodFamily medicineMEDLINEStatisticsNursing

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)0.099
metaresearch head score (Gemma)0.187
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.187
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0150.012
Science and technology studies0.0060.003
Scholarly communication0.0070.003
Open science0.0140.004
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0060.002

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 head0.482
GPT teacher head0.579
Teacher spread0.097 · 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 designQualitative
DomainMethods
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

Citations22
Published2019
Admission routes3
Has abstractno

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