Tipping the scale to no pain: Knowledge synthesis of procedural pain management in neonates
Bibliographic record
Abstract
Continual advances in neonatal intensive care have led to higher and higher survival rates for critically ill preterm and term neonates (1). As part of necessary and life-sustaining medical care, however, these infants are subjected to numerous painful skin-breaking needle procedures and pain (2). Acutely, pain causes suffering and clinical instability. In the long-term, pain can lead to suboptimal brain development, including reduced cortical grey matter, white matter and thalamic volume loss, alterations in pain responsivity, and motor and cognitive functioning (3–11). Clinical practice guidelines from professional organizations promote pain management in neonates undergoing painful medical procedures to reduce the documented harms. However, recommendations are vague and inconsistent at times (12). Pain mitigation is also promoted as a fundamental human right and aligned with the Hippocratic oath to ‘first do no harm’. Despite these resources and statements, hospitalized neonates continue to experience iatrogenic pain with no or minimal analgesia (13–16). Potential reasons for suboptimal pain care include the lack of high-quality evidence for analgesic interventions, lack of knowledge about pain management interventions, and dismissive and negative attitudes about pain management by clinicians (17,18).
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".