Procedural pain assessment in neonates at risk of neonatal opioid withdrawal syndrome: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to identify evidence on pain assessment during acute procedures in hospitalized neonates at risk of neonatal opioid withdrawal syndrome (NOWS). INTRODUCTION: While all neonates are routinely exposed to various painful procedures, neonates at risk of NOWS have longer hospital stays and are exposed to multiple painful procedures. NOWS occurs when a neonate is born to a birth parent who identifies as having sustained opioid use (such as morphine or methadone) during pregnancy. Accurate pain assessment and management during painful procedures is critical for minimizing the well-documented adverse effects of unmanaged pain in neonates. While pain indicators and composite pain scores are valid and reliable for healthy neonates, there is no review of evidence regarding procedural pain assessment in neonates at risk of NOWS. INCLUSION CRITERIA: Eligible studies will include those reporting on hospitalized pre-term and full-term neonates at risk of NOWS having pain assessments (ie, behavioral indicators, physiological indicators, validated composite pain scores) during and/or after exposure to an acute painful procedure. METHODS: This review will follow the JBI scoping review methodology. Databases to be searched will include MEDLINE (Ovid), CINAHL (EBSCO), Embase, PsyclNFO (EBSCO), and Scopus. The relevant data will be extracted by 2 reviewers using a modified JBI extraction tool. The results will be summarized in narrative and tabular format, including the components of participants, concept, and context (PCC). REVIEW REGISTRATION: Open Science Framework https://osf.io/fka8s .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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".