Bibliographic record
Abstract
OBJECTIVE: To evaluate epidemiological associations between self-reported diet pill consumption and major depressive episodes (MDEs), using data from a large-scale, cross-sectional survey of the Canadian population. METHODS: Data from the National Population Health Survey (NPHS) were used in this analysis. The NPHS interview included a brief version of the Composite International Diagnostic Interview (CIDI) depression section, known as the CIDI Short Form for Major Depression (CIDI-SFMD), as well as provision for self-reported medication use. RESULTS: Approximately 0.5% of the population reported the use of diet pills. Diet pill use was more common among women than among men. At the time of data collection (1996-1997), the most commonly used medication was fenfluramine (since withdrawn from the market because of cardiovascular toxicity). The use of these medications was strongly associated with MDE: the annual prevalence among persons reporting use was 17.1% (95% CI, 8.6 to 25.6), approximately 4 times the underlying population rate. CONCLUSIONS: Because the NPHS was a general health survey, and because self-reported exposure to these medications was relatively uncommon, the data did not permit a detailed multivariate analysis. These findings, however, indicate that depressive psychopathology is strongly associated with the use of appetite-suppressant medications.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".