Diabetes, cardiovascular disease, and health care use in people with and without schizophrenia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To compare the prevalence of cardiovascular risk factors (CV-RF) and disease (CV-D) and health care use in people with and without schizophrenia. SUBJECTS/MATERIALS AND METHODS: Data from the Canadian Community Health Survey (CCHS), cycle 3.1, were used. Prevalence of CV-RF, CV-D, and health care use were compared in those with and without schizophrenia using logistic regression analysis. Sampling weights and bootstrap variance estimates were used to account for survey design. RESULTS: A total of 399 (0.3%) people with schizophrenia were identified and compared to 120,044 (97.7%) people without. Individuals with schizophrenia were significantly more likely to be obese (34.8% vs. 15.6%) and report diabetes (11.9% vs. 5.3%). After accounting for sociodemographic variables, schizophrenia was not independently associated with diabetes (adjusted odds ratio [aOR]: 0.86; 0.49-1.51). Individuals with schizophrenia were more likely to be hospitalized (21.9% vs. 8.0%; aOR: 2.37; 95% CI: 1.51-3.74) but no more likely to visit their physician (86.7% vs. 85.7%; aOR: 1.23; 95% CI: 0.65-2.35). DISCUSSION/CONCLUSION: Our findings suggest that people with schizophrenia access the primary health care system at least as frequently as someone without schizophrenia, and the opportunity for management of modifiable CV-RF exists in this vulnerable population.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it