Risk Factors for New‐Onset Diabetes Mellitus in Patients Receiving Protease Inhibitor Therapy
Bibliographic record
Abstract
BACKGROUND: Metabolic complications including diabetes mellitus (DM) have been associated with protease inhibitor (PI) therapy. Risk factors for the development of DM are not well-defined. OBJECTIVES: To determine risk factors for the development of new-onset DM in patients initiated on PI therapy. METHODS: A retrospective cohort study was conducted to identify predictors of developing DM in subjects started on PI therapy between January 1997 and January 2003. Diabetes cases were defined as physician documentation of DM in the outpatient medical chart and/or those subjects receiving an antidiabetic agent. Logistic regression was used to examine the relationship between new-onset DM and demographic characteristics, and between new-onset DM and total treatment days with PI therapy. Body mass index could not be entered into the model due to missing height measurements. RESULTS: A total of 496 subjects on PI therapy were included, of which 18 (3.6%) developed DM. The mean age of the subjects was 43.4+/-9.4 years (range 19 to 77) and the mean duration of therapy was 3.0+/-1.9 years (range 0.17 to 7.9). In the multivariate model, older subjects were more likely to develop DM (OR 1.12, 95% CI 1.05 to 1.19; P=0.001). This corresponds to a 12% increased risk of DM for each one-year increase in age. Subjects that weighed more had an increased risk (OR 1.06, 95% CI 1.03 to 1.10; P=0.001), as did those belonging to a non-Aboriginal minority group when compared with Caucasians (OR 6.67, 95% CI 1.56 to 28.41; P=0.01). A longer duration of PI therapy was also significantly associated with developing DM (OR 1.52, 95% CI 1.07 to 2.17; P=0.02). CONCLUSION: A longer duration of PI therapy is associated with an increased risk of developing DM. As with HIV-negative subjects, demographic characteristics such as age, weight and ethnicity were important predictors of developing DM in the present study.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".