Early treatment response as a predictor of ongoing weight loss in obesity treatment
Bibliographic record
Abstract
OBJECTIVES: This study examined early treatment response in obesity treatment, defined as early change in body mass index (BMI) and early change in eating behaviour, as a predictor of ongoing weight loss in obese patients. METHODS: We conducted a repeated measures analysis of eating behaviour, emotional factors (depression, stress, perfectionism) and BMI, over a 9 month period. Subjects were 344 females, aged 18-65 (mean = 41.8), with a BMI of at least 25 (mean BMI = 33.7), engaged in very-low calorie (VLCD) or low-calorie (LCD) diets. RESULTS: Multi-level modelling identified four significant predictors of ongoing weight loss (weight loss occurring between 5 weeks and 9 months after the start of treatment). These included: type of diet, early BMI change (start to 5 weeks), number of weigh-ins and the early change in uncontrolled eating (start to 5 weeks). Estimates based on multi-level modelling indicate that patients with strong versus weak early treatment responses would be expected to show large differences in ongoing weight loss. CONCLUSIONS: Early improvements in eating behaviour and weight appear to have additive effects in the prediction of ongoing weight change. Future research is required to identify the optimal rate of weight loss, whether there are critical periods for behaviour change, and factors which influence the likelihood of early behaviour change.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".