Poster Session : PS 0770 ; Upper GI Tract : Effect of Intragastric Injection of Botulinum Toxin a for the Treatment of Obesity: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Controversies regarding effect of intragastric injection of botulinum toxin A for the treatment of obesity still remain. Methods: A systematic literature review was conducted using the core databases (PubMed, EMBASE, and the Cochrane Library). Pre- and post-treatment body weight data were extracted and analyzed using Hedges`s g. The analysis was performed divided by 2 ways which are pre-post comparative approach in botulinum injected group and comparison with placebo injected group. A random effect model was applied. The methodological quality of the enrolled studies was assessed by the Risk of Bias table and Newcastle-Ottawa Scale. Publication bias was evaluated through the funnel plot, trim and fi ll method, Egger`s test, and rank correlation test. Results: A total of 139 patients was enrolled from 8 studies (91 treated vs. 49 placebo group) and followed up for median 12 weeks (IQR: 6.5-16 weeks). Overall, treatment group was associated with weight loss in a pre-post comparative approach and compared to the placebo group (Hedges`s g: -0.619, 95% CI: -1.090, -0.148, P = 0.01; Hedges`s g: -0.650, 95% CI: -1.035, -0.265, P = 0.001). Wide area injection including fundus or body rather than the antrum only injection was associated with weight loss. Multiple injection number (above 10) was associated with weight loss. However, large amount of botulinum (more than 500 IU) was not associated with weight loss. Sensitivity analyses showed consistent results. Publication bias was not detected. Conclusions: In this analysis, intragastric injection of botulinum toxin A is effective for the treatment of obesity.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.003 |
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".