Child- and Proxy-reported Differences in Patient-reported Outcome and Experience Measures in Pediatric Surgery: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Purpose Pediatric health outcomes are often assessed using proxy reports, which may not fully capture children's experiences. Children with surgical conditions face unique, changing healthcare journeys, making accurate representation challenging. This review compares child-reported health status and treatment experiences from Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) with parent reports. Methods A systematic search, designed by a librarian and adhering to PRISMA guidelines, was conducted across eight databases up to July 2023, targeting studies using PROMs and PREMs in pediatric surgery to capture both child and parent perspectives. Two reviewers independently screened abstracts, with conflicts resolved by senior authors. The Mixed Methods Appraisal Tool (MMAT) was used for quality assessment. A meta-analysis was also performed on Pediatric Quality of Life Inventory (PedsQL™) outcomes. Results Of 5415 screened studies, 53 met inclusion criteria: 50 used PROMs, two used PREMs, and one used both. PedsQL™ appeared in 30 studies, with 16 other quality of life measures used less frequently. Twenty-two studies with PedsQL™ data from 6691 child-parent pairs were included in the meta-analysis. The pooled effect size between child- and parent-reported PedsQL™ scores was 0.98 (95 % CI: [-0.81, 2.77]), with high heterogeneity (I 2 = 89 %). Conclusion This review revealed substantial variability but minimal systematic differences between child and parent reports, highlighting the need for future research to understand this variability and improve integration of child and parent perspectives in pediatric health assessments. Level of evidence I, Systematic Review or meta-analysis of RCTs (randomized control trials).
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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 | 0.032 | 0.089 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".