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Record W2612951305 · doi:10.1503/cmaj.1095415

New guidelines for evaluating health tech

2017· article· en· W2612951305 on OpenAlexvenueaboutno aff
Donalee Moulton

Bibliographic record

VenueCanadian Medical Association Journal · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)Data scienceComputer scienceBest practiceHealth dataMedicineHealth carePolitical scienceSociology

Abstract

fetched live from OpenAlex

Researchers looking to consistently translate health data into an economic analysis that decision-makers can actually use will want to refer to the updated guidelines from the Canadian Agency for Drugs and Technologies in Health (CADTH). “The guidelines are best-practice standards,” says Karen

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)0.098
metaresearch head score (Gemma)0.278
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.278
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0200.018
Science and technology studies0.0030.005
Scholarly communication0.0140.010
Open science0.0090.007
Research integrity0.0120.018
Insufficient payload (model declined to judge)0.0310.021

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 head0.562
GPT teacher head0.538
Teacher spread0.024 · 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 designTheoretical or conceptual
DomainEvaluation
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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Medical Association Journal→Same topicHealth Systems, Economic Evaluations, Quality of Life→French-language works237,207→