Effect of contraindicated drugs for heart failure on hospitalization among seniors with heart failure
Bibliographic record
Abstract
Little is known about the effect of nonsteroidal anti-inflammatory drugs (NSAIDs), thiazolidinediones (TZDs), nifedipine and nondihydropyridine calcium channel blockers (CCBs) usage on the risk of all-cause hospitalization among seniors with heart failure (HF). We assessed the risk of all-cause hospitalization associated with exposure to each of these drug classes, in a population of seniors with HF.Using the Quebec provincial databases, we conducted a nested case-control study in a population of individuals aged ≥65 with a first HF diagnosis between 2000 and 2009. Patients were considered users of a potentially inappropriate drug class if their date of hospital admission occurred in the interval between the date of the last drug claim and the end date of its days' supply. The risks of hospitalization were estimated using multivariate conditional logistic regression.Of the 128,853 individuals included in the study population, 101,273 (78.6%) were hospitalized. When compared to nonusers, users of NSAIDs (adjusted odds ratio: 1.16; 95% confidence interval: 1.13-1.20), TZD (1.09; 1.04-1.14), and CCBs (1.03; 1.01-1.05) had an increased risk of all-cause hospitalization, but not the users of nifedipine (1.00; 0.97-1.03).Seniors with HF exposed to a potentially inappropriate drug class are at increased risk of worse health outcomes. Treatment alternatives should be considered, as they are available.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".