Levels of evidence in cardiovascular clinical practice guidelines.
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines (CPGs) can be helpful in distilling the medical research literature for clinicians; however, the guidelines should acknowledge the variable methodological quality used in clinical research by tempering their recommendations with a 'levels of evidence' scale. OBJECTIVE: To evaluate the proportion of English-language cardiovascular CPGs that provide the user with recommendations graded according to a defined levels of evidence scale. In addition, to evaluate other key aspects important in the critical appraisal of CPGs. METHODS: CPGs for atrial fibrillation, congestive heart failure and myocardial infarction were identified by searching MEDLINE, a reference text of CPGs and the Internet. Each CPG was evaluated using a priori-defined criteria based on the Evidence-Based Medicine Working Group's paper on critical appraisal of CPGs, including use of a reproducible search strategy, method of obtaining consensus, peer review and testing in practice. RESULTS: A total of 95 CPGs were evaluated. Only 13% graded their recommendations using a defined levels of evidence scale. In addition, few CPGs documented a reproducible search strategy or peer review process, and none had been formally tested in practice. CONCLUSIONS: Reporting the levels of evidence for recommendations is an important component of CPGs, yet this system is not widely used.
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 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 | 0.078 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
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".