The quality of paediatric asthma guidelines: evidence underpinning diagnostic test recommendations from a meta-epidemiological study
Bibliographic record
Abstract
BACKGROUND: Asthma is one of the most frequent reasons children visit a general practitioner (GP). The diagnosis of childhood asthma is challenging, and a variety of diagnostic tests for asthma exist. GPs may refer to clinical practice guidelines when deciding which tests, if any, are appropriate, but the quality of these guidelines is unknown. OBJECTIVES: To determine (i) the methodological quality and reporting of paediatric guidelines for the diagnosis of childhood asthma in primary care, and (ii) the strength of evidence supporting diagnostic test recommendations. DESIGN: Meta-epidemiological study of English-language guidelines from the United Kingdom and other high-income countries with comparable primary care systems including diagnostic testing recommendations for childhood asthma in primary care. The AGREE-II tool was used to assess the quality and reporting of the guidelines. The quality of the evidence was assessed using GRADE. RESULTS: Eleven guidelines met the eligibility criteria. The methodology and reporting quality varied across the AGREE II domains (median score 4.5 out of 7, range 2-6). The quality of evidence supporting diagnostic recommendations was generally of very low quality. All guidelines recommended the use of spirometry and reversibility testing for children aged ≥5 years, however, the recommended spirometry thresholds for diagnosis differed across guidelines. There were disagreements in testing recommendations for 3 of the 7 included tests. CONCLUSIONS: The variable quality of guidelines, lack of good quality evidence, and inconsistent recommendations for diagnostic tests may contribute to poor clinician adherence to guidelines and variation in testing for diagnosing childhood asthma.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad)Meta-epidemiology (narrow) Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.158 | 0.478 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.034 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".