Systematic review of the psychometric properties, interpretability and feasibility of self-report pain intensity measures for use in clinical trials in children and adolescents
Bibliographic record
Abstract
The aim of this study was to systematically review the psychometric properties, interpretability and feasibility of self-report pain intensity measures for children and adolescents for use in clinical trials evaluating pain treatments. Databases were searched for self-report measures of single-item ratings of pain intensity for children aged 3-18 years. A total of 34 single-item self-report measures were found. The measures' psychometric properties, interpretability and feasibility, were evaluated independently by two investigators according to a set of psychometric criteria. Six single-item measures met the a priori criteria and were included in the final analysis. While these six scales were determined as psychometrically sound and show evidence of responsivity, they had varying degrees of interpretability and feasibility. No single scale was found to be optimal for use with all types of pain or across the developmental age span. Specific recommendations regarding the most psychometrically sound and feasible measures based on age/developmental level and type of pain are discussed. Future research is needed to strengthen the measurement of pain in clinical trials with children.
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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 | 0.028 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".