Can we screen young children for their ability to provide accurate self-reports of pain?
Bibliographic record
Abstract
No validated screening tasks exist to distinguish children who can accurately use self-report pain measures from those who cannot. Children aged 3-7 years (n=108), each with a parent, provided data before and after day surgery. Parents rated how well they thought their child could understand the Faces Pain Scale-Revised (FPS-R), and children completed 4 screening tasks in counterbalanced order, such as rating pain in vignettes and selecting a middle-sized cup. Parents and children used the FPS-R to rate the children's pain intensity. Children's FPS-R ratings were scored for accuracy based on the extent to which they conformed to expected pain trajectories (e.g., pain increasing following surgery, decreasing following analgesia), and based on parent-child agreement. On average, parents rated the youngest age at which children could understand the FPS-R as 4.4 years (95% confidence interval 4.1-4.5). The youngest children provided inaccurate high pain ratings before surgery, but they became indistinguishable from the oldest in the accuracy of their pain ratings for the remainder of the 3-day study period, suggesting that direct experience with pain or with the rating task may improve accuracy. Although children's performance on the screening tasks was significantly associated with self-report accuracy, no prediction was strong enough for clinical use (all r's < 0.30). We failed to identify a screening tool that was better than chronological age in identifying which children could accurately self-report pain using the FPS-R. Future research should explore other screening tasks, training methods, and simplified approaches to pain assessment for young children. The ability to use self-report pain scales usually develops from age 3 to 7 years, but no valid screening method exists to identify this achievement.
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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 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".