A Systematic Review of Clinical Practice Guidelines for Neonatal Abstinence Syndrome
Bibliographic record
Abstract
BACKGROUND: The prevalence of neonatal abstinence syndrome is increasing, but the number and quality of clinical practice guidelines available are unknown. This systematic review aimed to identify, appraise and evaluate clinical practice guidelines for neonatal abstinence syndrome. METHODS: A systematic search of databases and the grey literature was conducted between 1 June and 1 July 2022. Full-text guidelines published by national or state-wide institutions were included. The recommendations from each guideline were extracted. The AGREE-II instrument was used to assess guideline quality. Sufficient-quality scores were defined as >60 and good-quality scores were >80 for each domain of AGREE-II. RESULTS: A total of 1703 records were identified, and 22 guidelines from the United States, Australia, Canada and the United Kingdom, published between 2012 to 2021, were included. The quality scores were low, with median scores of 37/100 for stakeholder involvement, 33/100 for methodology, 34/100 for applicability and 0 for editorial independence. Scope and purpose scored 72/100, and presentation scored 85/100. Sixteen (73%) guidelines did not meet the cut-offs for clinical use. CONCLUSION: Many guidelines were of insufficient quality to guide clinical practice for neonatal abstinence syndrome. This emphasises the need for high-quality studies to inform clinical practice guidelines, improve care and reduce the risk of poor outcomes in these high-risk infants.
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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 | 0.031 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".