CASE REPORT: Efficacy of Hoodia for weight loss: is there evidence to support the efficacy claims?
Bibliographic record
Abstract
Increasing rates of adult obesity and its negative health consequences are likely to become an increasing burden to the Canadian health care system. Consumers are looking for treatment options and often try the natural health products that are heavily promoted as safe, fast and effective. In this case report, MH, a 57-year-old overweight female wanted advice regarding whether she should use the natural product Hoodia to help her attain her weight loss goals. A literature search was conducted using Medline, EMBASE, the Cochrane Library, Natural Medicines Comprehensive Database and IPA from inception to March 2009. The internet, files of the authors and bibliographies of articles were searched for additional references. No published, peer-reviewed randomized controlled trials examining efficacy of Hoodia were found. Unpublished data from two small trials reported promising results with no adverse events. However, this leaves many unanswered questions regarding the use of Hoodia for weight loss such as the appropriate dose and duration, short and long term safety and use in patients with concomitant diseases. Literature suggests that some commercial products may not actually contain Hoodia at all. Additionally, Hoodia is not yet listed in the Canadian Licensed Natural Health Products Database meaning products sold in Canada may not meet Canadian regulatory standards. Upon discussing this information, MH decided not to use Hoodia, and other evidence-based recommendations were discussed.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".