Early weight loss in adolescents following bariatric surgery predicts weight loss at 12 and 24 months
Bibliographic record
Abstract
BACKGROUND: Growing evidence supports the efficacy of paediatric bariatric surgery. However, there is a paucity of data examining adolescent outcomes post surgery. Among adults, studies have shown that early weight loss is associated with long-term weight loss. Therefore, the aim of our study was to investigate the association between early weight loss at 3 months with longer-term weight loss at 12 and 24 months in adolescents post surgery. We hypothesized that patients who have greater weight loss within the first 3 months will have greater weight loss at 12 and 24 months post surgery. METHODS: A retrospective chart review of bariatric surgery patients (n = 28) was conducted. Anthropometric measurements at baseline and 3, 12, and 24 months were analysed. RESULTS: Percent of excess weight loss (%EWL) at 3, 12, and 24 months were 33.6 ± 11.3%, 55.0 ± 20.5%, and 55.1 ± 27.1%, respectively. %EWL at 3 months was positively associated with %EWL at 12 and 24 months (P < 0.05). Receiver operating characteristic curve results identified a cut-off of greater than or equal to 30%EWL at 3 months predicted successful weight loss, defined as greater than or equal to 50%EWL at 12 and 24 months. CONCLUSION: These findings demonstrate that majority of weight loss among adolescents occurs within the first postoperative year. Greater %EWL by 3 months post surgery predicts successful and sustained weight loss over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".