The Effect of Short Term Weight Loss Program in Iranian Obese Veterans
Bibliographic record
Abstract
BACKGROUND: To evaluate the effectiveness of a structured multidisciplinary non-surgical obesity therapy program on the weight loss of overweight and obese veterans after 6 weeks.METHODS: This is a prospective study carried out on 250 overweight and obese [Body Mass Index (BMI) ≥25] male veterans referred to a multidisciplinary weight control clinic of Sasan Hospital in Tehran, Iran, during 2012-2015. A low-calorie-diet was prescribed to obese veterans for 6 weeks, and additional intervention performed by psychologist, endocrinologist and sport medicine specialist. Weight, height and waist circumference (WC), were measured before and after intervention BMI was calculated.RESULTS: About 28% of participants had amputations or spinal cord injuries, more than 90% suffer from psychiatric problems and about 30% reported exposure to chemical weapons. 144 subjects with the mean age 47.0±5.9 years completed the study and remained for longitudinal analysis. No significant difference was observed between subjects followed and those lost to follow up, except for weight and BMI; subjects lost to follow up had higher weight and BMI, compared to subjects followed. The mean weight, WC and BMI of followed subjects were 95.1±19.2 kg, 109.0±13.2 cm and 32.0±6.1 kg/m2 respectively. At the end of study, reductions in weight, WC and BMI were statistically significant (0.7±0.8 kg, 2.1±2.4 cm and 2.2±2.9 kg/m2 respectively) (P<0.001).CONCLUSIONS: The present non-surgical intervention program is an effective treatment of obesity in veterans and could be a valuable basis for future weight maintenance strategies required for sustained 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".