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Record W3086655774 · doi:10.1136/bmj.m3216

Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials

2020· review· en· W3086655774 on OpenAlexaff
Janice L. Kwan, Lisha Lo, Jacob Ferguson, Hanna R. Goldberg, Juan Pablo Díaz-Martínez, George Tomlinson, Jeremy Grimshaw, Kaveh G Shojania

Bibliographic record

VenueBMJ · 2020
Typereview
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsHealth Sciences CentreOttawa HospitalUniversity of TorontoUniversity Health NetworkUniversity of OttawaSunnybrook Health Science CentreSinai Health System
Fundersnot available
KeywordsMedicineMeta-analysisClinical trialQuartileInterquartile rangeClinical decision support systemConfidence intervalMeta-regressionDecision support systemMEDLINEPsychological interventionRandomized controlled trialInternal medicineData miningComputer science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.067
metaresearch head score (Gemma)0.141
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.141
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0250.059
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.661
GPT teacher head0.682
Teacher spread0.021 · 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 designMeta-analysis
DomainMethods
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

Citations445
Published2020
Admission routes1
Has abstractyes

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