Major Bleeding in Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Few studies have examined risk factors for hemorrhage in hemodialysis patients. The contribution of warfarin and antiplatelet agent exposure to the incidence of first major bleeding episodes in hemodialysis patients was determined. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Retrospective chart review was performed in eligible hemodialysis patients. Incidence rates were determined as the number of first major bleeding events divided by the total exposure time on each treatment combination. Time-dependent covariates and Cox proportional hazard models were used to determine the hazard rate of having a first major bleeding event. RESULTS: A total of 1028 person-years of exposure were observed from 255 patients with a median follow-up time of 3.6 yr. The incidence rate of major bleeding episodes was 2.5% per person-year. The incidence of major bleeding episodes was 3.1% per person-year of warfarin exposure, 4.4% per person-year of aspirin exposure, and 6.3% per person-year of exposure to the combination of warfarin and aspirin. Compared with patients who were not prescribed warfarin or aspirin, the multivariable hazard ratio for time to first major bleeding event was 3.59 for warfarin, 5.24 for aspirin, and 6.19 for the combination of aspirin and warfarin. CONCLUSIONS: The risk for major bleeding episodes in hemodialysis patients increases significantly while on aspirin and/or warfarin, although warfarin alone did not reach statistical significance. Future studies should evaluate the efficacy of these agents in the secondary prevention of cardiovascular events in this high-risk population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".