Tricking the Brain with Leptin to Limit Post Liposuction and Post Bariatric Surgery Weight Regain?
Bibliographic record
Abstract
Obesity represents a medical challenge for modern therapists. The main difficulty is that once obesity is established, it is hard to reverse. It is believed that once an increased body weight/adiposity content is reached it becomes the "reference" that energy mechanisms adjust towards keeping. Thus, following a weight loss, such as following liposuction/bariatric surgery, the metabolic balance would target this "reference" that represents the previously reached body weight/adiposity content. On the other hand, medical procedures of liposuction and bariatric surgery reduce the level of the adipocytes-produced hormone leptin. This leptin level reduction leads to an increase in food intake and a decrease in energy expenditure. Therefore, the reduced leptin would be among the signals received by the brain to trigger weight regain via processes aiming to re-establish the pre-liposuction/pre-bariatric surgery body weight or adiposity content. We suggest administering leptin so that the brain does not detect the post- liposuction/post-bariatric surgery weight loss; thus, limiting the signals toward weight regain, leading to a better weight control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".