Incidences, temporal trends and risks of hospitalisation for gastrointestinal bleeding in new or chronic low-dose aspirin users after treatment for <i>Helicobacter pylori</i>: a territory-wide cohort study
Bibliographic record
Abstract
OBJECTIVE: (HP) eradication remains poorly defined. We characterised the incidences and temporal trends of hospitalisations for all GIB in aspirin users after HP eradication therapy. DESIGN: Based on a territory-wide health database, we identified all patients who had received the first course of clarithromycin-based triple therapy between 2003 and 2012. Patients were divided into three cohorts according to aspirin use: new users (commenced after HP eradication), chronic users (commenced before and resumed after HP eradication) and non-users. The primary outcome was to determine the risk of hospitalisation for GIB. RESULTS: We included 6985 new aspirin users, 5545 chronic users and 48 908 non-users. The age-adjusted and sex-adjusted incidence of hospitalisation for all GIB in new, chronic and non-users was 10.4, 7.2 and 4.6 per 1000 person-years, respectively. Upper and lower GIB accounted for 34.7% and 45.3% of all bleeding, respectively. Compared with chronic users, new users had a higher risk of GIB (HR with propensity score matching: 1.89; 95% CI 1.29 to 2.70). Landmark analysis showed that the increased risk in new aspirin users was only observed in the first 6 months for all GIB (HR 2.10, 95% CI 1.41 to 3.13) and upper GIB (HR 2.52, 95% CI 1.38 to 4.60), but not for lower GIB. CONCLUSION: New aspirin users had a higher risk of GIB than chronic aspirin users, particularly during the initial 6 months. Lower GIB is more frequent than upper GIB in aspirin users who had HP eradicated.
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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 | 0.002 |
| 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 | 0.001 |
| 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".