Bariatric Surgery for Morbid Obesity: Tehran Obesity Treatment Study (TOTS) Rationale and Study Design
Bibliographic record
Abstract
BACKGROUND: Obesity is a major health concern in the Middle East and worldwide. It is among the leading causes of morbidity, mortality, health care utilization, and costs. With bariatric surgery proving to be a more effective treatment option for overweight and obesity, the need for systematic assessment of different procedures and their outcomes becomes necessary. These procedures have not yet been described in detail in our region. OBJECTIVE: We aim to undertake a prospective study evaluating and comparing several surgical bariatric procedures in an Iranian population of morbid obese patients presenting to a specialized bariatric center. METHODS: In order to facilitate and accelerate understanding of obesity and its complications, the Tehran Obesity Treatment Study (TOTS) was planned and developed. This study is a longitudinal prospective cohort study in consecutive patients undergoing bariatric surgery. TOTS investigators use standardized definitions, high-fidelity data collection system, and validated instruments to gather data preoperatively, at the time of surgery, postoperatively, and in longer-term follow-up. RESULTS: This study has recruited 1050 participants as of September 2015 and is ongoing. CONCLUSIONS: This study will ensure creation of high-level evidence to enable clinicians to make meaningful evidence-based decisions for patient evaluation, selection for surgery, and follow-up care.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".