Five-year weight loss, physical activity, and eating style trajectories after bariatric surgery
Bibliographic record
Abstract
BACKGROUND: Little research has taken individual variability in weight loss into account. Furthermore, physical activity (PA) and eating style (ES) have been linked only sporadically to weight loss longitudinally. OBJECTIVES: Identify and describe latent classes of weight loss, change of PA, and change of ES up to 5 years after surgery and investigate whether these trajectories are interrelated. SETTING: Multicenter outpatient clinic. METHODS: This is a retrospective study of data collected during standard treatment before and up to 5 years after surgery. Latent class growth analysis was used to identify trajectories of weight loss (percent total weight loss), PA (Baecke questionnaire), and ES (Dutch Eating Behavior Questionnaire). RESULTS: A total of 2785 patients were included. Follow-up rate was 84% at 1 year and 34% at 5 years. Analyses revealed 5 weight loss trajectories. Most patients followed an average, fairly stable weight loss trajectory (48%) or an above-average partial-regain trajectory (36%). Other patients followed a low-responder trajectory (9%), a rapid weight loss and weight regain trajectory (6%), or a continued weight loss trajectory (2%). Patients in the most favorable weight loss trajectory were more likely to also follow the most favorable ES trajectories. Patients following the most unfavorable weight loss trajectory were never also in the PA trajectory with an initial great increase in PA. CONCLUSION: This study distinguishes demographic and behavioral factors that may influence long-term weight loss trajectories after bariatric surgery. Trajectories varied mainly in magnitude and less in the pattern of weight loss over time, suggesting that very deviant patterns are rare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".