Distal gastric bypass surgery for the treatment of hypothalamic obesity after childhood craniopharyngioma
Bibliographic record
Abstract
CONTEXT: Obesity resulting from damage to the hypothalamus, i.e. hypothalamic obesity, is a severe condition that currently lacks any effective evidence-based therapy. OBJECTIVE: Our goal was to describe the course of hypothalamic obesity in a craniopharyngioma patient treated with distal gastric bypass surgery and to outline distinct aspects of multidisciplinary case management. PATIENT AND METHODS: A 29-year-old man, who had undergone craniopharyngioma resection at the age of 8, was referred to our Interdisciplinary Obesity Centre with a body mass index (BMI) of 52.0 kg/m(2), type 2 diabetes and obstructive sleep apnoea syndrome (OSAS). After careful preoperative preparation, including the adjustment of hormone substitution therapy for panhypopituitarism, nutritional counselling and a supervised exercise program, he underwent a distal gastric bypass operation. RESULTS: Eighteen months after the operation the patient's BMI had decreased to 31.9 kg/m(2), type 2 diabetes was in complete remission, and OSAS appeared to be improved. Also, feelings of hunger were markedly reduced after the operation. A standard regimen of supplements successfully prevented any severe nutritional deficiencies. After weight loss, the dose of hydrocortisone could be distinctly reduced without any signs of adrenal insufficiency while GH substitution had to be markedly increased to achieve normal IGF1 levels. CONCLUSIONS: Our case demonstrates that within a multidisciplinary team approach, a distal gastric bypass operation can be a safe and highly effective therapy for patients with hypothalamic obesity. Also, our findings hint at an effect of gastric bypass surgery on hunger and eating behaviour that may not essentially rely on hypothalamic mechanisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".