Maternal and iatrogenic neonatal opioid withdrawal syndrome: Differences and similarities in recognition, management, and consequences
Bibliographic record
Abstract
Opioids are potent analgesics used to manage pain in both young and old, but the increased use in the pregnant population has significant individual and societal implications. Infants dependent on opioids, either through maternal or iatrogenic exposure, undergo neonatal opioid withdrawal syndrome (NOWS), where they may experience withdrawal symptoms ranging from mild to severe. We present a detailed and original review of NOWS caused by maternal opioid exposure (mNOWS) and iatrogenic opioid intake (iNOWS). While these two entities have been assessed entirely separately, recognition and treatment of the clinical manifestations of NOWS overlap. Neonatal risk factors such as age, genetic predisposition, drug type, and clinical factors like type of opioid, cumulative dose of opioid exposure, and disease status affect the incidence of both mNOWS and iNOWS, as well as their severity. Recognition of withdrawal is dependent on clinical assessment of symptoms, and the use of clinical assessment tools designed to determine the need for pharmacotherapy. Treatment of NOWS relies on a combination of non-pharmacological therapies and pharmacological options. Long-term consequences of opioids and NOWS continue to generate controversy, with some evidence of anatomic brain changes, but conflicting animal and human clinical evidence of significant cognitive or behavioral impacts on school-age children. We highlight the current knowledge on clinically relevant recognition, treatment, and consequences of NOWS, and identify new advances in clinical management of the neonate. This review brings a unique clinical perspective and critically analyzes gaps between the clinical problem and our preclinical understanding of NOWS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".