Identification of pain indicators for infants at risk for neurological impairment: A Delphi consensus study
Bibliographic record
Abstract
BACKGROUND: A number of infant pain measures have been developed over the past 15 years incorporating behavioural and physiologic indicators; however, no reliable or valid measure exists for infants who are at risk for neurological impairments (NI). The objective of this study was to establish consensus about which behavioural, physiologic and contextual indicators best characterize pain in infants at high, moderate and low levels of risk for NI. METHODS: A 39- item, self-administered electronic survey that included infant physiologic, behavioral and contextual pain indicators was used in a two round Delphi consensus exercise. Fourteen pediatric pain experts were polled individually and anonymously on the importance and usefulness of the pain indicators for the 3 differing levels of risk for NI. RESULTS: The strength of agreement between expert raters was moderate in Round 1 and fair in Round 2. In general, pain indicators with the highest concordance for all three groups were brow bulge, facial grimace, eye squeeze, and inconsolability. Increased heart rate from baseline in the moderate and severe groups demonstrated high concordance. In the severe risk group, fluctuations in heart rate and reduced oxygen saturation were also highly rated. CONCLUSION: These data constitute the first step in contributing to the development and validation of a pain measure for infants at risk for NI. In future research, we will integrate these findings with the opinions of (a) health care providers about the importance and usefulness of infant pain indicators and (b) the pain responses of infants at mild, moderate and high risk for NI.
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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 | 0.159 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".