Assessment and management of acute pain in high-risk neonates
Bibliographic record
Abstract
Neonates in the neonatal intensive care unit experience hundreds of painful procedures at a time of rapid neurological development. Although the immediate responses to pain may be protective, the potential long-term effects of early and under-treated pain are concerning. As pain assessment is the first step in the provision of appropriate and timely pain management, attention should be directed to the quantification of pain in terms of its location, severity, intensity and duration. Over the past decade, numerous pain measures have been developed for preterm and term neonates, however, most of them have been developed for research purposes and have not been tested in the clinical setting. In order to effectively implement pain measures in the clinical setting, the psychometric properties of reliability, validity, feasibility and clinical utility must be established. This review paper will highlight the importance of neonatal pain assessment and examine the psychometric properties of various measures of neonatal pain. Pharmacological and non-pharmacological interventions to manage acute pain in high-risk neonates will be addressed and future research topics will be proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".