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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".