Diagnosis and Management of Childhood Obesity in a Canadian Academic Family Medicine Teaching Unit
Bibliographic record
Abstract
Rationale: The aim of this study was to use the clinical practice guidelines issued by the Quebec provincial government to assess the diagnosis and management of childhood obesity in a Canadian academic family medicine teaching unit (FMTU). Methods: We performed an audit of diagnosis and care of childhood obesity in a FMTU in the province of Quebec. We used the clinical practice guidelines established by the Quebec government’s Institut national d’excellence en santé et en services sociaux (INESSS) which include use of the World Health Organization (WHO) growth chart for diagnosing childhood obesity. We analyzed the electronic medical records of every child from 5 to 12 years old who had a medical appointment at the FMTU in 2017 (n=618). We audited whether childhood obesity had been correctly diagnosed according to the WHO growth chart and if the medical care that followed was adequate according to INESSS guidelines. Results: We identified 71 children as obese according to the WHO chart, of whom 40 (56%) had been diagnosed as such by clinic health professionals. Of these 40, (33) 83% received nutritional counseling, (33) 83% received physical activity counseling, (13) 33% had parent’s involvement counselling, (19) 48% were referred to another health professional (e.g., dietician, psychologist, kinesiologist) and (12) 31% were followed up within six months. Only 7 (18%) patients received all INESSS’s recommendations. Conclusions: Our study shows that childhood obesity remains under-diagnosed in Canadian primary care, even in an academic teaching environment. This affects the quality of care delivered to these patients. Moreover, even if childhood obesity had been correctly diagnosed, management of this clinical condition is still incomplete. Understanding barriers and facilitators to diagnosing and managing childhood obesity is necessary to improve the quality of care on a larger scale.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".