Evidence for overuse of cardiovascular healthcare services in high-income countries: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Overuse of cardiovascular healthcare services, defined as the provision of low-value (ineffective, harmful, cost-ineffective) tests, medications and procedures, may be common and associated with increased patient harm and health system inefficiencies and costs. We seek to systematically review the evidence for overuse of different cardiovascular healthcare services in high-income countries. METHODS AND ANALYSIS: We will search MEDLINE, EMBASE and Evidence-Based Medicine Reviews from 2010 onwards. Two investigators will independently review titles and abstracts and full-text studies. We will include published English-language studies conducted in high-income countries that enrolled adults (mean/median age ≥18 years) and reported the incidence or prevalence of overuse of cardiovascular tests, medications or procedures; adjusted risk factors for overuse; or adjusted associations between overuse and outcomes (reported estimates of morbidity, mortality, costs or lengths of hospital stay). Acceptable methods of defining low-value care will include literature review and multidisciplinary iterative panel processes, healthcare services with reproducible evidence of a lack of benefit or harm, or clinical practice guideline or Choosing Wisely recommendations. Two investigators will independently extract data and evaluate study risk of bias in duplicate. We will calculate summary estimates of the incidence and prevalence of overuse of different cardiovascular healthcare services across studies unstratified and stratified by country; method of defining low-value care; the percentage of included females, different races, and those with low and high socioeconomic status or cardiovascular risk; and study risks of bias using random-effects models. We will also calculate pooled estimates of adjusted risk factors for overuse and adjusted associations between overuse and outcomes overall and stratified by country using random-effects models. We will use the Grading of Recommendations, Assessment, Development and Evaluation to determine certainty in estimates. ETHICS AND DISSEMINATION: No ethics approval is required for this study as it deals with published data. Results will be presented at meetings and published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42021257490.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.120 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.029 | 0.041 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".