Bibliographic record
Abstract
BACKGROUND: Obesity is worldwide one of today's most important medical and public health problems. Orlistat (Xenical) is a relatively new drug in the pharmacological treatment of obesity which partially blocks fat absorption. The following article analyses the available evidence of orlistat's effectiveness in the treatment of obese patients. METHODS: Three randomised controlled trials investigating the effect of orlistat in the treatment of obesity were identified by systematic Medline search. The internal and external validity of these studies was assessed using systematic criteria. RESULTS: All three studies consistently demonstrate a treatment benefit of orlistat compared to placebo. Patients treated with orlistat lost an average of 3.4 kg more than patients taking placebo while on a hypocaloric diet. Simultaneously, control of cardiovascular risk factors improved independently of the observed weight loss. Up to 40% of all patients experienced gastrointestinal side effects which were generally well tolerated. The studies prove that treatment with orlistat can result in a moderate weight reduction. However, the results of the studies cannot be easily generalised to obese patients in a primary care setting, due to limitations concerning the studies' internal and external validity. CONCLUSIONS: Based on these studies orlistat is an efficient pharmacological treatment for obesity in patients adhering to a hypocaloric diet. Studies demonstrating orlistat's effectiveness in a primary care setting are so far lacking. From a public health perspective there is a need for a randomised controlled trial showing not only orlistat's effectiveness on surrogate markers in a primary care setting but, ideally, a reduction in obesity-related mortality and morbidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".