Childhood obesity diagnosis and management remains a challenge despite the use of electronic health records: A retrospective study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it