Methodological assessment of Mexican Clinical Practice Guidelines: Critical appraisal and GRADE framework adherence.
Bibliographic record
Abstract
Background and Objective Clinical Practice Guidelines (CPGs) provide evidence-based recommendations to healthcare professionals, policy makers, patients and other stakeholders. Mexico is the biggest producer of CPGs in Latin America and Caribbean countries. The National Healthcare Technology Excellence Center (acronym in Spanish: CENETEC) is responsible for the CPG development, adaptation and update. The aim of this study was to assess the Mexican CPG quality and adherence to the GRADE framework. Study design We conducted a descriptive cross-sectional study of 86 CPGs representing all the CPGs produced by CENETEC between 2015 and 2017 and published in an online database called “Catalogo Maestro”. We performed quality assessment with the online AGREE II tool and assessed the reporting on the GRADE framework. Results Of the 86 CPGs, 34 were published in 2015, 21 in 2016 and 31 in 2017. The overall quality by AGREE II proved a median of 16.6% (Min 16.6%, Max 50%). Of the 86 CPGs, 25 (29%) used the GRADE framework; adherence to GRADE standards was, however, inconsistent and generally poor. Conclusion CPGs produced by CENETEC during this period had a low score by AGREE II standards and low adherence to the GRADE framework. A concerted initiative could rapidly improve CENETEC guidelines.
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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 | 0.452 | 0.782 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".