Dose-Finding, Safety, and Tolerability Study of an Oral Platelet Glycoprotein IIb/IIIa Inhibitor, Lotrafiban, in Patients With Coronary or Cerebral Atherosclerotic Disease
Bibliographic record
Abstract
BACKGROUND: Antiplatelet therapy is the mainstay of the treatment and secondary prevention of cardiovascular and cerebrovascular ischemic events. We assessed the safety, tolerability, and pharmacodynamics of lotrafiban, an oral platelet glycoprotein IIb/IIIa inhibitor, as a secondary prevention strategy in patients with cerebrovascular or cardiovascular disease. METHODS AND RESULTS: Overall, 451 patients with a recent cardiovascular or cerebrovascular acute ischemic event were randomized in a double-blind fashion to 1 of 5 dosing regimens for 12 weeks: placebo or 5, 20, 50, or 100 mg lotrafiban, both twice daily with 300 to 325 mg/d aspirin. The primary end point was the incidence and tolerability of major and minor bleeding during treatment. Secondary end points included inhibition of platelet aggregation and clinical events. The placebo and lotrafiban 5-mg groups had similarly low rates of minor and major bleeding, but the 100-mg arm was terminated early because of excess major bleeding. Protocol-defined thrombocytopenia (<100 000 platelets/microL) occurred in 5 lotrafiban-treated patients (1.4%, 95% CI 0.2% to 2.7%) and 1 placebo patient (1.1%, 95% CI 0% to 3.1%). Three lotrafiban-treated patients had a nadir platelet count <20 000/microL (0.9%, 95% CI 0% to 1.8%). Lotrafiban produced dose-dependent inhibition of platelet aggregation; 5 mg lotrafiban did not differ significantly from placebo, whereas 100 mg inhibited aggregation by nearly 100%. CONCLUSIONS: -Lotrafiban provides dose-dependent platelet inhibition when administered to a range of patients with atherosclerosis. The level of platelet inhibition appears to correlate with bleeding risk and drug tolerability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".