Impact of using patient-reported outcome measures in routine clinical care of paediatric patients with chronic conditions: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Chronic diseases among children are associated with lower health-related quality of life (HRQOL) and higher utilisation of healthcare services. Integrating Patient-Reported Outcomes Measures (PROMs) in routine clinical care has been shown to reduce utilisation of healthcare services while improving patient outcomes. The objectives of our study are to: (1) identify previously implemented and evaluated PROMs for chronic conditions in paediatric settings; (2) consolidate the evidence to evaluate the impact of using PROMs on HRQOL, healthcare utilisation, patient outcomes (eg, symptoms control) and quality of care among paediatric patients with chronic conditions. The findings from this review will inform the future integration of PROMs in paediatric clinical practice. METHODS AND ANALYSIS: We will systematically search the following electronic databases: MEDLINE, EMBASE, CINAHL, PsychINFO and Cochrane library. Reference lists of included studies will also be searched in Web of Science (Thomson Reuters) database to ensure more complete coverage. Two reviewers will independently screen the studies and abstract the data using standardised form. Extracted data will be analysed and synthesised. Finally, a narrative synthesis of summarised data will be presented. ETHICS AND DISSEMINATION: Ethical approval is not required, as the proposed systematic review will use data from published research articles. The results of this study will be disseminated through publication in peer-reviewed journals, scientific conferences and meetings, and the lead author's doctoral dissertation. PROSPERO REGISTRATION NUMBER: CRD42018109035.
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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 | 0.090 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.014 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
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".