Outpatient evaluation, recognition, and initial management of pediatric overweight and obesity in U.S. military medical treatment facilities
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: As childhood obesity is a concern in many communities, this study investigated outpatient evaluation and initial management of overweight and obese pediatric patients in U.S. military medical treatment facilities (MTFs). METHODS: Samples of 579 overweight and 341 obese patients (as determined by body mass index [BMI]) aged 3-17 years were drawn from MTFs. All available FY2011 outpatient records were searched for documentation of BMI assessment, overweight/obesity diagnosis, and counseling. Administrative data for these patients were merged to assess coded diagnostic and counseling rates and receipt of recommended laboratory screenings. CONCLUSIONS: Generic BMI documentation was high, but BMI percentile assessments were found among fewer than half the patients. Diagnostic recording or recognition totaled 10.9% of overweight and 32.0% of obese. Counseling rates were higher, with 46.4% and 61.0% of overweight and obese patients, respectively, receiving weight related counseling. Among patients 10 years of age or older, rates of recommended lab screenings for diabetes, liver abnormality, and dyslipidemia were not greater than 33%. BMI percentile recording was strongly associated with diagnostic recording, and diagnostic recording was strongly associated with counseling. IMPLICATIONS FOR PRACTICE: Improvements to electronic health records or implementation of local procedures to facilitate better diagnostic recording would likely improve adherence to clinical practice guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".