Hematologic dyscrasia associated with ticlopidine therapy: evidence for causality.
Bibliographic record
Abstract
BACKGROUND: Several rare, potentially fatal types of hematologic dyscrasia, such as agranulocytosis, aplastic anemia, neutropenia, pancytopenia, thrombocytopenia and thrombotic thrombocytopenic purpura (TTP), have been associated with ticlopidine therapy. The extent to which ticlopidine is the causative factor has not been addressed quantitatively. METHODS: We identified 211 published case reports of hematologic dyscrasia associated with ticlopidine therapy from a MEDLINE search. We analyzed the 91 reports that could be evaluated, using the Bayesian Adverse Reaction Diagnostic Instrument to calculate the posterior probability that ticlopidine caused the hematologic dyscrasia based on epidemiologic and clinical trial data (prior odds) and case information (likelihood ratio). RESULTS: The median posterior probability values (and range) for agranulocytosis, aplastic anemia, neutropenia, pancytopenia, thrombocytopenia and TTP were 0.95 (0.53-0.98), 0.81 (0.57-0.93), 0.86 (0.75-0.96), 0.78 (0.61-0.89), 0.74 (0-0.92) and 1.0 (0.33-1.00) respectively. The posterior probability was 0.75 or greater in 82 (90%) of the case reports. INTERPRETATION: This systematic analysis provides stronger evidence to implicate ticlopidine as the causative factor in the various types of hematologic dyscrasia in most published case reports.
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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 | 0.017 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".