Children’s Self‐Report of Pain Intensity: What We Know, Where We Are Headed
Bibliographic record
Abstract
The present paper provides a short, practical introduction to children's self-report measures of pain intensity, followed by an overview of principles and issues. Details on individual self-report scales were previously reported in a landmark systematic review in 2006 and will not be repeated here. Broader measurement issues discussed here include interpretation of pain scores over time, across individuals and in relation to contextual factors; special considerations affecting children younger than six years of age; social communicative functions of pain reports; cognitive developmental factors in understanding pain scales and their anchors; screening for the ability to use self-report scales and training for children who do not have this skill; level of measurement (interval versus ordinal); estimating clinically significant change for groups and individuals; and measurement of aspects of pain other than intensity. Also highlighted are areas in which there has been progress and a lack of progress since the last time this topic was featured at the International Forum on Pediatric Pain in 1996. The present article closes with an outline of key areas for further research on children's self-report of pain and a brief summary of recommendations for clinicians.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".