Hospitalization for gastrointestinal adverse events attributable to the use of low‐dose aspirin among patients 50 years or older also using non‐steroidal anti‐inflammatory drugs: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Use of aspirin with non-steroidal anti-inflammatory drugs increases the risk of gastrointestinal ulcers; however, it is not clear if this risk varies with the non-steroidal anti-inflammatory drug used. AIM: To assess the risk of gastrointestinal hospitalizations attributable to aspirin in patients 50 years or older also using non-steroidal anti-inflammatory drugs. METHODS: Administrative data of patients 50 years or older who received a non-steroidal anti-inflammatory drug or acetaminophen prescription between 1998 and 2004 were used. RESULTS: Study patients received 7,412,992 non-steroidal anti-inflammatory drug prescriptions and 5,614,044 acetaminophen prescriptions among which 23% and 32%, respectively, were dispensed to aspirin users. Time-dependent Cox regression models revealed that, compared to patients using acetaminophen (without aspirin), the adjusted hazard ratio (95% CI) among non-users of aspirin were: rofecoxib 1.3 (1.2, 1.5), celecoxib 0.7 (0.6, 0.8), diclofenac 1.5 (1.2, 1.7), ibuprofen 0.9 (0.6, 1.4), naproxen 2.5 (2.1, 3.0) and piroxicam 1.5 (0.8, 2.8); among users of aspirin: rofecoxib 3.2 (2.8, 3.7), celecoxib 1.8 (1.5, 2.1), diclofenac 2.8 (2.2, 3.5), ibuprofen 1.4 (0.8, 2.7), naproxen 2.2 (1.6, 3.0) and piroxicam 2.0 (0.8, 5.4). The risk attributable to aspirin varied from none with naproxen to 61% (53%, 68%) with celecoxib. CONCLUSION: The increase in gastrointestinal hospitalization attributable to aspirin differed with the non-steroidal anti-inflammatory drug used, and seemed higher with cyclo-oxygenase-2 inhibitors than with non-selective non-steroidal anti-inflammatory drugs.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".