The quality of diagnostic guidelines for children in primary care: A meta‐epidemiological study
Bibliographic record
Abstract
AIM: To determine the quality of paediatric guidelines relevant to diagnosis of three of the most common conditions in primary care: fever, gastroenteritis and constipation. METHODS: We undertook a meta-epidemiological study of paediatric guidelines for fever, gastroenteritis and gastroenteritis. We systematically searched MEDLINE, Embase, Trip Database, Guidelines International Network, the National Guideline Clearinghouse and WHO from February 2011 to September 2022 for guidelines from high-income settings containing diagnostic recommendations. We assessed the quality of guideline reporting for included guidelines using the AGREE II tool. RESULTS: We included 16 guidelines: fever (n = 7); constipation (n = 4) and gastroenteritis (n = 5). The overall quality across the three conditions was graded moderate (median AGREE II score 4.5/7, range 2.5-6.5) with constipation guidelines rated the highest (median 6/7), and fever rated the lowest (median 3.8/7). Major methodological weaknesses included consideration of guideline applicability. Half of the guidelines did not report involving parent representatives, and 56% did not adequately declare or address their competing interests. CONCLUSIONS: Substantial variations exist in the quality of paediatric guidelines related to the diagnosis of primary care presentations. Better quality guidance is needed for general practitioners to improve diagnosis for children in primary care.
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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 | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".