The effect of participation in a weight loss programme on short‐term health resource utilization
Bibliographic record
Abstract
Obese people consume significantly greater amounts of health resources. This study set out to determine if health resource utilization by obese people decreases after losing weight in a comprehensive medically supervised weight management programme. Four hundred and fifty-six patients enrolled in a single-centred, multifaceted weight loss programme in a universal health care system were studied. Patient information was anonymously linked with administrative databases to measure health resource utilization for 1 year before and after the programme. Mean body mass index (BMI) decreased by more than 15%. The mean annual physician visits (pre = 9.6, post = 9.4) did not change significantly after the programme. However, patients saw a significantly fewer number of different physicians per year following the programme (pre = 4.5, post = 3.9; P < 0.001). Mean annual number of emergency visits (pre = 0.2; post = 0.2) and hospital admissions (pre = 0.05; post = 0.08) did not change. Neither baseline BMI, nor its change during the programme, influenced changes in health resource utilization. Our study suggests that weight loss in a supervised weight management programme does not necessarily decrease short-term health resource utilization. Further study is required to determine if patients who maintain their weight loss experience a decrease in health utilization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".