Bibliographic record
Abstract
A severely obese 28 year old woman (body mass index (BMI) 37.9) with type 2 diabetes, controlled hypertension, and sleep apnoea is seeking your advice about weight loss. She has lost 6 kg over the past year by reducing portion sizes, but her weight has recently plateaued. She does not want to consider bariatric surgery and asks instead about drug treatments. Current treatment for obesity consists primarily of health behaviour modification (diet, exercise, and behavioural therapy) for all patients and bariatric surgery for a minority of selected severely obese people.1 Because health behaviour modification is unsuccessful in many patients, and the availability of bariatric surgery is limited, additional adjunctive, effective, and safe obesity treatments are needed. To date, antiobesity drugs have not adequately filled this therapeutic void. The serotonergic agents fenfluramine and dexfenfluramine were withdrawn in 1997 because of associations with cardiac valvulopathy and pulmonary hypertension.2 After the withdrawals of rimonabant ( Acomplia ) in 2009 for depression and suicidal ideation, and sibutramine ( Meridia, Reductil ) in 2010 because of increased cardiovascular risk, orlistat became the only agent available for long term weight management. In 2012, two new oral agents—phentermine and extended release (ER) topiramate ( Qsymia ) and lorcaserin ( Belviq )—were approved by the US Food and Drug Administration as adjuvants to health behaviour modification in patients with a BMI of greater than 30 or greater than 27 if they also had an obesity related comorbidity, such as hypertension, dyslipidaemia, or type 2 diabetes. As discussed elsewhere, the European Medicines Agency did not approve either agent, citing toxicity concerns and a lack of morbidity and mortality data.3 Here, we provide a clinically focused summary to guide GPs in the use of these drugs. ### Orlistat This inhibitor of gastric and pancreatic lipase prevents intestinal fat metabolism and absorption.4 Prescription orlistat ( Xenical ) has been …
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".