Outcome domains and pain outcome measures in randomized controlled trials of interventions for postoperative pain in children and adolescents
Bibliographic record
Abstract
BACKGROUND: We analysed outcome domains and pain outcome measures in randomized controlled trials of interventions for postoperative pain management in children and adolescents and compared them to the core outcome set recommended by the Pediatric Initiative on Methods, Measurement and Pain Assessment in Clinical Trials (PedIMMPACT). METHODS: Systematic literature search was conducted in MEDLINE, CDSR, DARE, CINAHL and PsycINFO up to 31 January 2017. One author extracted data and second verified the extraction. Outcome domains and pain outcome measures were analysed and compared with the PedIMMPACT core outcome set. RESULTS: We included 337 trials. Median number of reported outcomes was five (range 1-11) for the included trials and two (range 0-6) for PedIMMPACT. The most commonly analysed PedIMMPACT outcome domains were pain intensity (93%) and "symptoms and adverse events" (83%). The remaining four PedIMMPACT outcomes were present in under 30% of included randomized controlled trials. Proportion of PedIMMPACT outcome domains did not change after the PedIMMPACT was published in 2008. Of the 312 trials that reported pain intensity, 303 (97%) also specified pain assessment tools, in which the most common was the visual analogue scale (24%) followed by the Children's Hospital of Eastern Ontario Pain Scale (18%). CONCLUSION: Analysed trials about interventions for pediatric postoperative pain insufficiently used the recommended core outcome set for acute pain in children. Relevance of the PedIMMPACT core outcome set, as well as the reasons behind its limited uptake, need to be further evaluated. SIGNIFICANCE: Recommended core outcomes have been insufficiently used in randomized controlled trials about postoperative pain in children, which hinders comparability of studies and makes synthesis of evidence difficult.
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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 | 0.168 | 0.452 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".