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Record W1694700009 · doi:10.1002/14651858.cd009401

Interventions to improve the use of systematic reviews for clinical and commissioning decision-making

2011· reference-entry· en· W1694700009 on OpenAlexaff
Lakshmi Murthy, Sasha Shepperd, Mike Clarke, Sarah Garner, John N. Lavis, Nia Roberts

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2011
Typereference-entry
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychological interventionProtocol (science)MedicineProject commissioningSystematic reviewIntervention (counseling)Cochrane collaborationManagement scienceProcess managementKnowledge managementMEDLINEAlternative medicinePublishingNursingRandomized controlled trialCochrane LibraryEngineeringComputer scienceSurgeryPolitical science

Abstract

fetched live from OpenAlex

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To identify and assess the effects of information packages and organisational interventions designed to support the uptake of systematic review evidence by health system managers, policy makers and clinicians. In addition, the conceptual framework used to categorise the types of interventions will be tested.

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)0.364
metaresearch head score (Gemma)0.559
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.636
Threshold uncertainty score0.784

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3640.559
Meta-epidemiology (narrow)0.0070.011
Meta-epidemiology (broad)0.0130.018
Bibliometrics0.0440.037
Science and technology studies0.0040.005
Scholarly communication0.0110.015
Open science0.0070.015
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.1250.023

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 head0.856
GPT teacher head0.593
Teacher spread0.263 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
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

Citations3
Published2011
Admission routes1
Has abstractyes

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