Predictors of early attrition and successful weight loss in patients attending an obesity management program
Bibliographic record
Abstract
BACKGROUND: Our objective was to identify factors that are independently associated with early attrition and successful weight loss (WL) in an obesity-management program. METHODS: Participants were 9,498 patients enrolled in treatment at the Wharton Weight Management Clinic for at least 6 months. Predictors of early attrition (<6 months) and successful WL (≥5 %) were analyzed using relative risk (RR) in men and women separately. Pearson's correlation was used to determine the relationship between WL and treatment time Weight loss and attrition analysis was restricted to patients who had more than two visits (n = 5415). RESULTS: Older individuals had lower early attrition (RR Range:0.74-0.92, P < 0.05) and greater WL success (RR Range:1.40-1.65, P < 0.05) than younger individuals. Males with hypertension and females with depression had greater early attrition (RR Range:1.09-1.20, P < 0.05) and lower WL success (RR Range:0.48-0.57, P < 0.05) than those without these health conditions. Males with lower education had greater early attrition (RR = 1.11[1.03-1.19]) than males with higher education, but did not differ in WL. Females who smoked had greater early attrition (RR = 1.06[1.01-1.11]) than females who did not smoke, but did not differ in WL. Ethnicity was not related to early attrition, however, females of Black and Other ethnicities had lower WL success compared to White females (RR Range:0.58-0.74, P < 0.05). After adjusting for treatment time, all above associations were no longer significant and treatment time remained as the only independent predictor of WL success (P < 0.0001). CONCLUSION: As WL is positively and independently related with treatment time, individuals at risk for early attrition may need alternative treatment options, in order to improve patient retention and improve WL success.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".