A Core Outcome Set for Neonatal Opioid Withdrawal Syndrome
Bibliographic record
Abstract
BACKGROUND: As rates of neonatal opioid withdrawal are increasing, the need for research to evaluate new treatments is growing. Large heterogeneity exists in health outcomes reported in current literature. Our objective is to develop an evidence-informed and consensus-based core outcome set in neonatal opioid withdrawal syndrome (NOWS-COS) for use in studies and clinical practice. METHODS: An international multidisciplinary steering committee was established. A systematic review and a 3-round Delphi was performed with open-ended and score-based assessments of the importance of each outcome to inform clinical management of neonatal opioid withdrawal. Interviews were conducted with parents and/or caregivers on outcome importance. Finally, a consensus meeting with diverse stakeholders was held to review all data from all sources and establish a core set of outcomes with definitions. RESULTS: The NOWS-COS was informed by 47 published studies, 41 Delphi participants, and 6 parent interviews. There were 63 outcomes evaluated. Final core outcomes include (1) pharmacologic treatment, (2) total dose of opioid treatment, (3) duration of treatment, (4) adjuvant therapy, (5) feeding difficulties, (6) consolability, (7) time to adequate symptom control, (8) parent-infant bonding, (9) duration of time the neonate spent in the hospital, (10) breastfeeding, (11) weight gain at hospital discharge, (12) readmission to hospital for withdrawal, and (13) neurodevelopment. CONCLUSIONS: We developed an evidence-informed and consensus-based core outcome set. Implementation of this core outcome set will reduce heterogeneity between studies and facilitate evidence-based decision-making. Future research will disseminate all the findings and pilot test the validity of the NOWS-COS in additional countries and populations to increase generalizability and impact.
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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 | 0.113 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".