Peer Review #2 of "Successful childhood obesity management in primary care in Canada: what are the odds? (v0.1)"
Bibliographic record
Abstract
Background: The management of a child presenting with obesity in a primary care setting can be viewed as a multi-step behavioral process with many perceived and actual barriers for families and primary care providers.In order to achieve the goal of behavior change and, ultimately, clinically meaningful weight management outcomes in a child who is considered obese, all steps in this process should ideally be completed.We sought to review the evidence for completing each step, and to estimate the population effect of secondary prevention of childhood obesity in Canada.Methods: Data from the 2009/2010 Canadian Community Health Survey and from a review of the literature were used to estimate the probabilities for completion of each step.A flow chart based on these probabilities was used to determine the proportion of children with obesity that would undergo and achieve clinically meaningful weight management outcomes each year in Canada.Results: We estimated that the probability of a child in Canada who presents as obese achieving clinically meaningful weight management outcomes through secondary prevention in primary care is around 0.6% per year, with a range from 0.01% to 7.2% per year.The lack of accessible and effective weight management programs appeared to be the most important bottleneck in the process.Conclusions: In order to make progress towards supporting effective pediatric obesity management, efforts should focus on population-based primary prevention and a systems approach to change our obesogenic society, alongside the allocation of resources toward weight management approaches that are comprehensively offered, equitably distributed and robustly evaluated.
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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 | 0.033 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.204 | 0.069 |
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".