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Record W6939680593 · doi:10.6084/m9.figshare.23647519

Additional file 3 of Evaluating the effectiveness of a multifaceted intervention to reduce low-value care in adults hospitalized following trauma: a protocol for a pragmatic cluster randomized controlled trial

2023· article· en· W6939680593 on OpenAlexaff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of CalgaryUniversity of TorontoOttawa HospitalUniversité de MontréalInstitut National d'Excellence en Santé et en Services SociauxUniversité Laval
Fundersnot available
KeywordsTable (database)Protocol (science)Randomized controlled trialIntervention (counseling)Cluster (spacecraft)Matching (statistics)Sample (material)Cluster randomised controlled trialPrimary care

Abstract

fetched live from OpenAlex

Additional file 3: Table S1. PRECIS-2 scores for trial domains with rationale. Figure S1. Integration of the project within UK Medical Research Council guidelines for the Development of Complex Interventions. Table S2. Matching of barriers with implementation strategies according to the CFIR-ERIC tool. Table S3. Schedule of enrolment, interventions, and assessments. Table 4. Intervention prototype as per the TIDieR checklist. Figure S2.1. Example of a page of the A&F report for one quality indicator (intervention arm). Figure S2.2. Example of output from patient chart revision tool† (intervention arm). Table S5. Questions for the semi-structured interviews for focus groups (intervention refinement). Table S6. Sample size calculation for primary and selected secondary outcomes.

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)0.018
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.051
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.7610.054

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 head0.294
GPT teacher head0.555
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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