Characteristics and quality assessment of GRADE practice guidelines on maternal-fetal care
Bibliographic record
Abstract
The study aimed to assess the quality and applicability of current maternal-fetal health clinical practice guidelines that countries can adopt or adapt. A systematic search was conducted in the International Database of GRADE Guidelines (BIGG) for practice guidelines developed with the GRADE system (Grades of Recommendation, Assessment, Development, and Evaluation) and related to maternal-fetal care. The selected guidelines were evaluated with the AGREE-REX (Appraisal of Guidelines REsearch and Evaluation-Recommendations Excellence) tool to assess clinical applicability (domain-1), values and preferences (domain-2) and applicability (domain-3). The variables were presented descriptively, and a statistical analysis was performed on the domains according to institution and country of origin. Of 1,212 clinical practice guidelines, 72 met the inclusion criteria. According to the type of collaborating organization, the World Health Organization predominated with 58.3%, versus specialized medical societies. Domain 1, "Clinical applicability," was the best rated by the reviewers (68.5%) compared to domain 2, "Values and preferences" (60%). According to the type of institution that developed the clinical practice guideline, a significant difference was demonstrated in domains 1 (p= 0.000), 2 (p= 0.006) and 3 (p= 0.000). Only domains 1 (p= 0.000) and 3 (p= 0.018) were statistically significant based on country of origin. This study emphasizes the importance of improving the quality of maternal-fetal clinical practice guidelines developed by organizations and governmental institutions and the need to strengthen the institutionalization of the use of evidence to develop, adapt and implement practice guidelines in countries such as the United Kingdom, Canada, Spain, Colombia, the United States, among others.
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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 | 0.081 | 0.412 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".