Effect of Concomitant Drug Use on the Onset and Exacerbation of Diabetes Mellitus in Everolimus-Treated Cancer
Bibliographic record
Abstract
PURPOSE: Everolimus-induced diabetes mellitus (DM) outcomes include everolimus-resistant tumors and poor hyperglycemia outcomes, which lead to various other negative clinical outcomes. This study aimed to evaluate the effect of associations between concomitant drug treatment and time to DM event occurrence (onset or exacerbation) on the outcomes of everolimus-induced DM in patients with cancer. METHODS: Data from the Japanese Adverse Drug Event Report database (JADER) were used, and patient drug use, time of DM event occurrence, and DM outcomes were determined from patient records. Associations between concomitant drug groups with everolimus and DM event occurrence were then evaluated for patients with both good and poor DM outcomes. RESULTS: Top ten groups used concomitantly were drugs for the treatment of hypertension (HT), controlled DM, constipation, hypothyroidism, kidney disease, insomnia, hyperlipidemia, hyperuricemia, anemia, and gastritis. Among them, only HT, controlled DM, and hyperlipidemia were associated with DM event occurrence. These three drug groups were examined by the outcome of everolimus concomitant usage and revealed a significantly shorter time to DM event occurrence for patients with poor outcomes than for those with good outcomes (p = 0.015) among patients without a concomitant drug for DM. Each of these three drug groups was analyzed on patients who were concomitantly administered with one of each drug group with everolimus and revealed a significantly shorter time to DM event occurrence for patients with poor outcomes than for those with good outcomes in patients who received concomitant HT drugs (p = 0.006). Moreover, among the four HT drug categories, calcium channel blockers were significantly associated with poor outcomes (odds ratio, 2.18 [1.09-4.34], p = 0.028). CONCLUSION: To prevent everolimus-induced poor DM outcomes, early DM detection and treatment are necessary, and the effect of the concomitant drug should be considered before initiating everolimus treatment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".