Variability in infant acute pain responding meaningfully obscured by averaging pain responses
Bibliographic record
Abstract
Given the inherent variability in pain responding, using an "average" pain score may pose serious threats to internal and external validity. Using growth mixture modeling (GMM), this article first examines whether infants can be differentiated into stable groups based on their pain response patterns over a 2-minute post-needle period. Secondary analyses, to specifically address the issue of averaging pain scores to represent a sample, qualitatively described clinically meaningful differences between pain scores of the discerned groups and the overall mean (irrespective of groups). Infants were part of Canadian longitudinal cohort naturalistically observed during their 2-, 4-, 6-, and/or 12-month immunization appointments (N=458 to 574) at 3 pediatrician clinics between 2007 and 2012. At every age, GMM analyses discerned distinct groups of infants with significantly variable patterns of pain responding over the 2minutes post-needle. Our secondary suggested that the overall mean pain score immediately post-needle reflected most groups well at every age. However, for older infants (6 and 12months, especially), the overall mean pain responses at 1 and 2minutes post-needle significantly over or underestimated groups that contained 48% to 100% of the sample. These results combined highlight the significant variability of infant pain responding patterns between groups of infants and furthermore, calls into question the validity of using an overall mean in research with older infants during the regulatory phase post-needle.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".