Procedural Pain Scale Evaluation (PROPoSE) study: protocol for an evaluation of the psychometric properties of behavioural pain scales for the assessment of procedural pain in infants and children aged 6–42 months
Bibliographic record
Abstract
INTRODUCTION: Infants and children are frequently exposed to painful medical procedures such as immunisation, blood sampling and intravenous access. Over 40 scales for pain assessment are available, many designed for neonatal or postoperative pain. What is not well understood is how well these scales perform when used to assess procedural pain in infants and children. AIM: The aim of this study was to test the psychometric and practical properties of the Face, Legs, Activity, Cry and Consolability (FLACC) scale, the Modified Behavioural Pain Scale (MBPS) and the Visual Analogue Scale (VAS) observer pain scale to quantify procedural pain intensity in infants and children aged from 6-42 months to determine their suitability for clinical and research purposes. METHODS AND ANALYSIS: A prospective observational non-interventional study conducted at a single centre. The psychometric and practical performance of the FLACC scale, MBPS and the VAS observer pain scale and VAS observer distress scale used to assess children experiencing procedural pain will be assessed. Infants and young children aged 6-42 months undergoing one of four painful and/or distressing procedures were recruited and the procedure digitally video recorded. Clinicians and psychologists will be recruited to independently apply the scales to these video recordings to establish intrarater and inter-rater reliability, convergent validity responsiveness and specificity. Pain score distributions will be presented descriptively; reliability will be assessed using the intraclass correlation coefficient and Bland-Altman plots. Spearman correlations will be used to assess convergence and linear mixed modelling to explore the responsiveness of the scales to pain and their capacity to distinguish between pain and distress. ETHICS AND DISSEMINATION: Ethical approval was provided by the Royal Children's Hospital Human Research Ethics Committee, approval number 35220B. The findings of this study will be disseminated via peer-reviewed journals and presented at international conferences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.017 |
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".