Procedural Pain Assessments for Neonates at Risk of Neonatal Opioid Withdrawal Syndrome
Bibliographic record
Abstract
OBJECTIVES: To identify evidence for pain assessment during acute procedures in hospitalized neonates at risk of neonatal opioid withdrawal syndrome (NOWS). METHODS: This scoping review was conducted using the JBI scoping review methodology. The search strategy focused on identifying in-patient neonates undergoing acute painful procedures. Databases searched are MEDLINE, CINAHL, Embase, PsycInfo, and Scopus. The relevant data were extracted by 2 reviewers and the results were summarized in a narrative description and presented in a tabular format, including the components of participants, concept, and context. RESULTS: A total of 22,731 unique studies were screened, with 5 studies ultimately included. Of these studies, 2 included neonates at risk of NOWS but did not report pain responses separately. The 3 remaining studies observed procedural pain in opioid-exposed neonates compared with neonates without opioid exposure during heel lance. Pain assessment methods included physiological responses and validated composite pain scores. When using composite pain tools, 1 study showed higher pain response in opioid-exposed neonates, while the other 2 studies showed the same or lower pain response. For skin conductance, the findings from 2 studies were discrepant, with 1 study reporting higher pain response in opioid-exposed neonates and the other showing no statistically significant difference. DISCUSSION: There is a need for more studies designed to examine the influence of opioid exposure and withdrawal on pain responding and management in neonates. As there is currently limited evidence to guide clinical care, clinicians should continue to use validated composite pain assessment tools and pain management strategies.
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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 | 0.012 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".