Analysis of the validation of existing behavioral pain and distress scales for use in the procedural setting
Bibliographic record
Abstract
BACKGROUND: Assessing procedural pain and distress in young children is difficult. A number of behavior-based pain and distress scales exist which can be used in preverbal and early-verbal children, and these are validated in particular settings and to variable degrees. METHODS: We identified validated preverbal and early-verbal behavioral pain and distress scales and critically analysed the validation and reliability testing of these scales as well as their use in procedural pain and distress research. We analysed in detail six behavioral pain and distress scales: Children's Hospital of Eastern Ontario Pain Scale (CHEOPS), Faces Legs Activity Cry Consolability Pain Scale (FLACC), Toddler Preschooler Postoperative Pain Scale (TPPPS), Preverbal Early Verbal Pediatric Pain Scale (PEPPS), the observer Visual Analog Scale (VASobs) and the Observation Scale of Behavioral Distress (OSBD). RESULTS: Despite their use in procedural pain studies none of the behavioral pain scales reviewed had been adequately validated in the procedural setting and validation of the single distress scale was limited. CONCLUSIONS: There is a need to validate behavioral pain and distress scales for procedural use in preverbal or early-verbal 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".