Hemorrhage induced by Thrombolysis: new insights from a real-world pharmacovigilance study
Bibliographic record
Abstract
Abstract Background: Hemorrhage represents the most common and serious side effect of thrombolytic therapy. Many studies have compared the risk of bleeding between different thrombolytic therapy, but analysis of time-to-onset for hemorrhage induced by thrombolysis is yet sparse. Methods: We conducted a retrospective study based on the adverse drug reaction reports on antithrombotic agents collected by the Henan Adverse Drug Reaction Monitoring Center. We assessed the reporting odds ratio to determine the disproportionate reporting signals for bleeding and the Weibull shape parameter was used to evaluate the time-to-onset data. Results: In the signal detection, crude low molecular weight heparin-hemorrhage was found as a positive signal. The hemorrhage for most antithrombotic agents was random failure profiles. In particular, the hazard of hemorrhage decreased over time for warfarin and clopidogrel and increased for alteplase, nadroparin, and dipyridamole. Conclusions: We found that the patients taking Crude low molecular weight heparins were more likely to bear hemorrhagic side effects, which may be attributed to the severely irrational use of this medication under improper management. The risk of bleeding decreased over time for warfarin and clopidogrel and increased for alteplase, nadroparin, and dipyridamole.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".