Influence of risk of neurological impairment and procedure invasiveness on health professionals’ management of procedural pain in neonates
Bibliographic record
Abstract
OBJECTIVES: To describe how (i) risk of neurological impairment (NI) and (ii) procedure invasiveness influence health professionals' assessment and management of procedural pain in neonates in the Neonatal Intensive Care Unit (NICU). DESIGN: Prospective observational study. SETTING: Three tertiary level NICUs in Canada. PARTICIPANTS: 114 neonates, 25-40 weeks gestational age (GA) undergoing painful procedures. MAIN OUTCOME MEASURES: Physical and behavioural pain indicators and pharmacological and nonpharmacological pain interventions. RESULTS: 114 neonates at high (Cohort A, n=35), moderate (Cohort B, n=25) and low (Cohort C, n=54) risk of NI were observed during 254 painful procedures performed by 147 health professionals. Physical pain indicators were used more frequently by health professionals to assess pain with Cohorts A and B than C (p<.05). Behavioural pain indicators were used similarly across Cohorts. Nonpharmacological interventions were implemented most frequently for pain management. Physical interventions were used with 84% of procedures across Cohorts; particularly for the most invasive procedures. Infants with the highest NI risk (Cohort A) received the most behavioural interventions (p<.05) irrespective of procedural invasiveness. Pharmacological interventions were implemented with 23.2% of procedures; Cohort B received pharmacological interventions most frequently (Cohort B>A, B>C, p<.05) and for increasingly invasive procedures (p<.05). CONCLUSIONS: Health professionals use multidimensional indicators to assess neonatal pain. Nonpharmacological interventions dominate pain management. NI risk status and procedure invasiveness are important contextual factors in neonatal pain assessment and management.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".