A Newly Developed Application To Support Busy Clinicians In Providing Diagnostic-specific Physical Activity And Healthy Lifestyle Recommendations
Bibliographic record
Abstract
PURPOSE: Physical inactivity and poor diet led to an epidemic of preventable chronic diseases; due to shortage of primary care physicians, this led to over-reliance on non-urgent Emergency Department visits causing significant economic burden estimated at $32 billion annually. Additionally, nearly 34% of all patients discharged are readmitted within 90 days. Moreover, 79% of readmissions are preventable and estimated at $28 billion annually. Nevertheless, a physically active lifestyle is correlated with reduced prevalence of chronic diseases and reduced hospital admissions. An efficient strategy to promote physical activity may reduce the burden on healthcare services and related costs. METHODS: We created an application to assist clinicians in providing diagnosis-specific lifestyle recommendations. It includes (1) physical activity guidelines designed to inspire movement; (2) healthy eating strategies with a grocery list; and (3) coaching section for developing healthy behaviors; the recommendations are delivered in either (a) single page summary or (b) more comprehensive detailed information; all written in a coaching format to support patients maintain healthy behaviors. RESULTS: This application was designed to allow clinicians simple and rapid access to prepared recommendations for a healthy and active lifestyle tailored to the patient’s precise medical conditions. The clinician easily search the app for a specific diagnosis, for example “hypertension”; The application will then present explicit physical activity guidelines and dietary recommendations which are precise to the medical diagnosis. The recommendations are designed in simple terms and ready to print, for direct distribution from the clinician to the patient. CONCLUSIONS: The application is expected to promote implementation of physical activity health recommendations. Designed for busy clinicians, the app provides rapid and simple access to prepared health recommendations, ready to use. It requires minimal effort by the clinician in providing detailed and diagnostic-specific health recommendations. Efficient promotion of health recommendations is expected to modify the development, management, and even reversal of preventable chronic diseases, thus reducing use of healthcare resources and related costs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.037 |
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".