MétaCan
Menu
Back to cohort
Record W3007099010 · doi:10.1016/j.cjca.2020.02.072

Cardiac Rehabilitation Programs for Chronic Heart Disease: A Bayesian Network Meta-analysis

2020· article· en· W3007099010 on OpenAlexvenueno aff
Rongzhong Huang, Suetonia C. Palmer, Yu Cao, Hong Zhang, Yang Sun, Wenhua Su, Liwen Liang, Sanrong Wang, Ying Wang, Yu Xu, Narayan D. Melgiri, Lihong Jiang, Giovanni FM Strippoli, Xingsheng Li

Bibliographic record

VenueCanadian Journal of Cardiology · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNational Institute for Health and Care Research
KeywordsMedicineRehabilitationMyocardial infarctionRandomized controlled trialOdds ratioMeta-analysisPhysical therapyPercutaneous coronary interventionCoronary artery diseaseInternal medicineRelative riskCochrane LibraryCardiologyConfidence interval

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.048
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.088
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0160.062
Bibliometrics0.0050.004
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0050.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0100.001

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 head0.055
GPT teacher head0.334
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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

Citations53
Published2020
Admission routes1
Has abstractno

Explore more

Same venueCanadian Journal of CardiologySame topicCardiac Health and Mental HealthFrench-language works237,207