Assessing the Quality of Randomized Controlled Trials Examining Psychological Interventions for Pediatric Procedural Pain: Recommendations for Quality Improvement
Bibliographic record
Abstract
OBJECTIVE: Systematic reviews of randomized controlled trials (RCTs) support the efficacy of psychological interventions for procedural pain management. However, methodological limitations (e.g., inadequate randomization) have affected the quality of this research, thereby weakening RCT findings. METHODS: Detailed quality coding was conducted on 28 RCTs included in a systematic review of psychological interventions for pediatric procedural pain. RESULTS: The majority of RCTs were of poor to low quality (criteria reported in <50% of RCTs). Commonly reported criteria addressed study background, conditions, statistical analyses, and interpretation of results. Commonly nonreported criteria included treatment administration, evaluation of treatment efficacy (effect sizes, summary statistics, intention-to-treat analyses), caregiver demographics, follow-up, and participant flow. Quality was greater in more recent trials, and did not vary by journal type (psychology vs. medical). CONCLUSION: Despite poor quality ratings, quality reporting in psychological RCTs for pediatric procedural pain has improved over time. Recommendations for quality enhancement are provided.
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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 | 0.653 | 0.862 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".