Summary Proceedings From the Neonatal Pain-Control Group
Bibliographic record
Abstract
Recent advances in neurobiology and clinical medicine have established that the fetus and newborn may experience acute, established, and chronic pain. They respond to such noxious stimuli by a series of complex biochemical, physiologic, and behavioral alterations. Studies have concluded that controlling pain experience is beneficial with respect to short-term and perhaps long-term outcomes. Yet, pain-control measures are adopted infrequently because of unresolved scientific issues and lack of appreciation for the need for control of pain and its long-term sequelae during the critical phases of neurologic maturation in the preterm and term newborn. The neonatal pain-control group, as part of the Newborn Drug Development Initiative (NDDI) Workshop I, addressed these concerns. The specific issues addressed were (1) management of pain associated with invasive procedures, (2) provision of sedation and analgesia during mechanical ventilation, and (3) mitigation of pain and stress responses during and after surgery in the newborn infant. The cross-cutting themes addressed within each category included (1) clinical-trial designs, (2) drug prioritization, (3) ethical constraints, (4) gaps in our knowledge, and (5) future research needs. This article provides a summary of the discussions and deliberations. Full-length articles on procedural pain, sedation and analgesia for ventilated infants, perioperative pain, and study designs for neonatal pain research were published in Clinical Therapeutics (June 2005).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.017 |
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".