Patient-Reported Outcome Measures in Routine Pediatric Clinical Care: A Systematic Review
Bibliographic record
Abstract
Introduction: Integration of PROMs (Patient-reported outcome measures) in routine clinical care is growing but lacks consolidated evidence around its impact in pediatric care. This systematic review aims to evaluate the impact of integrating PROMs in routine pediatric clinical care on various outcomes in pediatric clinical care. Data Sources: MEDLINE, EMBASE, CINAHL, PsychINFO and Cochrane library. Web of Science database was searched selectively to ensure extended coverage. Study Selection: We included longitudinal studies reporting on integration of PROMs in routine pediatric clinical care of chronic diseases. Studies in languages other than English, published prior to the year 2000, and reporting on secondary data were excluded. Data Extraction: Two reviewers independently extracted data from included studies. Extracted data included citation of each study, type of healthcare setting, location of the study, characteristics of patient population, type of chronic disease, name and type of PROM, mode of administration and reported outcomes. Results: Out of 6869 articles, titles and abstracts of 5416 articles and full text of 23 articles were screened in duplicate. Seven articles reporting results from 6 studies met eligibility criteria. Integration of PROMs increased identification and discussion around HRQOL, especially in psychosocial and emotional domains, but showed mixed results with the impact on quality of care. No studies assessed the impact of integrating PROMs on healthcare utilization. Limitations: Due to significant heterogeneity in the studies, a meta-analysis was not conducted. Conclusions: Integrating PROMs could have a positive impact on HRQOL however, further studies are required to determine the impact of PROMs in routine pediatric clinical care.
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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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".