The CANadian Pediatric Weight management Registry (CANPWR): lessons learned from developing and initiating a national, multi-centre study embedded in pediatric clinical practice
Bibliographic record
Abstract
Abstract Background There is increasing recognition of the value of “real-world evidence” in evaluating health care services. Registry-based, observational studies conducted in clinical settings represent a relevant model to achieve this directive. Starting in 2010, we undertook a longitudinal, observational study (the CANadian Pediatric Weight management Registry [CANPWR]), which is embedded in 10 multidisciplinary, pediatric weight management clinics across Canada. The objective of this paper was to share the lessons our team learned from this multi-centre project. Methods Data sources included a retrospective review of minutes from 120 teleconferences with research staff and investigators, notes taken during clinical site visits made by project leaders, information from quality control processes to ensure data accuracy and completeness, and a study-specific survey that was sent to all sites to solicit feedback from research team members (n = 9). Through an iterative process, the writing group identified key themes that surfaced during review of these information sources and final lessons learned were developed. Results Several key lessons emerged from our research, including the (1) value of pilot studies and central research coordination, (2) need for effective and regular communication, (3) importance of consensus on determining outcome measures, (4) challenge of embedding research within clinical practice, and (5) difficulty in recruiting and retaining participants. The sites were, in spite of these challenges, enthusiastic about the benefits of participating in multi-centre collaborative studies. Conclusion Despite some challenges, multi-centre observational studies embedded in pediatric weight management clinics are feasible and can contribute important, practical insights into the effectiveness of health services for managing pediatric obesity in real-world settings.
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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 | 0.574 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.012 | 0.020 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".