Childhood obesity diagnosis and management remains a challenge despite the use of electronic health records: A retrospective study
Bibliographic record
Abstract
Background: The use of electronic health records (EHR) has revolutionized medical practice by improving the quality of care. Childhood obesity (CO) increases the risk of developing other chronic diseases and has a serious psychosocial impact on children. Using EHR may improve this clinical condition since early diagnosis is a crucial means of preventing its negative impacts. Objectives: The aim of the study was to assess the diagnosis and management of CO in a Canadian academic family medicine group unit (FMG-U) that uses EHR with an integrated CO diagnosis tool. Methods: = 618) were analyzed. EHR use by clinicians was assessed by a closed-ended online survey sent to clinicians who provided pediatric care at that clinic in 2017. Results: We identified 69 patients as obese according to the WHO, of whom 40 had been diagnosed by health professionals at the clinic. Of these, 33 received nutritional counseling; 33 received physical activity counseling; 13 received parent involvement counseling; 19 were referred to another health professional; and 12 were followed up within 6 months. Ten out of 15 clinicians responded to the survey. They all used the EHR integrated CO diagnosis tool but only 20% were truly familiar with it. Conclusions: This study shows that CO is still underdiagnosed in primary care, notwithstanding the use of EHR with integrated tools. This affects the quality of care. Moreover, even if CO were correctly diagnosed, its management remains incomplete. Knowledge translation by medical organizations plays an important role in addressing this problem.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".