Previous and posterior psychopharmacological treatment in bariatric surgery patients
Bibliographic record
Abstract
Introduction Bariatric surgery is an effective treatment for obesity. It has been demonstrated that it improves the prognosis of vascular risk factors. However, the long term effect of surgery on psychiatric pathology, as depression, and the treatment adjustment needed is not clear. Aim To describe the previous and posterior psychopharmacological treatment of patients operated of bariatric surgery in Hospital del Mar. Material and methods We used a database of 292 bariatric surgery patients who have been operated in Hospital del Mar from January 2010 to November 2015. In this database, sociodemographic information, psychiatric antecedents, and anterior and posterior treatments among other data are included. We have made a descriptive analysis about more used treatments and their evolution. Results In the sample, 27.1% of patients started with some psychiatric treatment the months before the bariatric surgery (16.4% had already a previous treatment prescribed). The medications the most frequently started before the surgery were selective serotonin reuptake inhibitors (SSRI, 11%), second were benzodiazepines and third a combination of the two previous treatments. Among antidepressants, Fluoxetine was the most prescribed (45.5%). Six months after surgery, 72.9% of patients were not taking any treatment. Conclusion The large variety of psychiatric drugs used in our sample indicates that clearer guidelines are needed about the most appropriated treatments for those patients. Further studies on the impact of this surgery on pathologies and their psychopharmacological treatments are needed. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".