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Record W6926641999 · doi:10.25384/sage.23564376

sj-docx-1-ctj-10.1177_17407745231182010 – Supplemental material for Chronic pain trials often exclude people with comorbid depressive symptoms: A secondary analysis of 346 randomized controlled trials

2023· article· en· W6926641999 on OpenAlexaff

Bibliographic record

VenueSage Journals Data · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsHumber River Regional HospitalInstitute for Clinical Evaluative SciencesUniversity of Toronto
Fundersnot available
KeywordsRandomized controlled trialChronic painClinical trialDepression (economics)ComorbidityN of 1 trial

Abstract

fetched live from OpenAlex

Supplemental material, sj-docx-1-ctj-10.1177_17407745231182010 for Chronic pain trials often exclude people with comorbid depressive symptoms: A secondary analysis of 346 randomized controlled trials by Darren K Cheng, Maarij Hannan Ullah, Henry Gage, Rahim Moineddin and Abhimanyu Sud in Clinical Trials

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)0.014
metaresearch head score (Gemma)0.141
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.986
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.141
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0070.011
Science and technology studies0.0010.001
Scholarly communication0.0080.006
Open science0.0040.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.9470.541

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 head0.038
GPT teacher head0.366
Teacher spread0.328 · 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
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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