A core outcome set for neonatal abstinence syndrome: study protocol for a systematic review, parent interviews and a Delphi survey
Bibliographic record
Abstract
BACKGROUND: The prevalence of neonatal abstinence syndrome (NAS) is increasing globally resulting in an increased incidence of adverse neonatal outcomes and health system costs. Evidence regarding the effectiveness of NAS prevention and management strategies is very weak and further research initiatives are critically needed to support meta-analysis and clinical practice guidelines. In NAS research, the choice of outcomes and the use of valid, responsive and feasible measurement instruments are crucial. There is currently no consensus and evidence-based core outcome set (COS) for NAS. METHODS/DESIGN: The development of the NAS-COS will include five stages led by an international Multidisciplinary Steering Committee: (1) qualitative interviews with parents/families and a systematic review (SR) to identify items for inclusion in a COS. The SR will also identify participants for the Delphi survey, (2) a three-round Delphi survey to gain expert opinion on the importance of health outcomes influencing NAS management decisions, (3), a consensus meeting to finalize the items and definitions with experts and COS users, (4) feasibility and pilot testing, development of the COS and explanatory document and (5) implementation planning. DISCUSSION: Since standardized outcome measurement and reporting will improve NAS clinical research consistency, efficacy and impact, this COS will reflect the minimum set of health outcomes which should be measured in trials evaluating interventions for preventing or treating NAS.
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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 | 0.196 | 0.150 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".