Critical appraisal of international guidelines for the screening and treatment of asymptomatic peripheral artery disease: a systematic review
Bibliographic record
Abstract
BACKGROUND: Peripheral artery disease (PAD) is often asymptomatic but increases the risk of developing cardiovascular events. Due to the uncertainties regarding the quality of related guidelines and a lack of clear-cut evidence, we performed a systematic review and critical appraisal of these guidelines to evaluate their consistency of the recommendations in asymptomatic PAD population. METHODS: Guidelines in English between January 1st, 2000 to December 31th, 2017 were screened in databases including Medline via PubMed, EMBASE, the G-I-N International Guideline Library, the National Guidelines Clearinghouse, the Canadian Medication Association Infobase and the National Library for Health. Those guidelines containing recommendations on screening and treatment for asymptomatic PAD were included, and three reviewers evaluated the quality of the guidelines using Appraisal of Guidelines Research and Evaluation (AGREE) II instrument. Related recommendations were then fully extracted and compared by two reviewers. RESULTS: Fourteen guidelines were included finally and the AGREE scores ranged from 39 to 73%. Most of included guidelines scored low in Rigor of development and Editorial independence, and only two guidelines (ACCF/AHA, AHA/ACC) reached the standard on Conflict of Interest from Institute of Medicine (IOM). Eight guidelines recommended screening at different strength while the others found insufficient evidence or were against screening. Conflicting recommendations on treatment were found in the target value of the lipid lowering and antiplatelet therapy. The treatment policies in three guidelines (BWG, CEVF, ESC) appeared more aggressive, but they had low transparency between guideline developer and industry or did not reach the standard of IOM. CONCLUSIONS: Current guidelines on asymptomatic PAD varied in the methodological quality and fell short of the standard in the rigor of development and editorial independence. Conflicting recommendations were found both on the screening and treatment. More effort is needed to provide clear-cut evidences with high quality and transparency among guideline developer and industry.
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 | 0.119 | 0.416 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.034 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".