Rapid‐Onset Thrombocytopenia Following Piperacillin‐Tazobactam Reexposure
Bibliographic record
Abstract
Drug-induced thrombocytopenia is a rare but serious adverse event that has been associated with multiple drugs including β-lactams. Although it mostly occurs with prolonged medication use, some cases of rapid-onset thrombocytopenia have been reported. We describe the case of a 69-year-old man who developed severe and immediate thrombocytopenia following reexposure to piperacillin-tazobactam in the critical care setting. He received a 6-day course of piperacillin-tazobactam for a possible pneumonia immediately after cardiac surgery. During this course of therapy, his platelet count decreased (fluctuating between 69 × 10(3) /mm(3) and 104 × 10(3) /mm(3) ) and then progressively increased after completion of the antibiotic to 340 × 10(3) /mm(3) on postoperative day 15. Ten days after the antibiotic course was completed (postoperative day 16), the patient developed new signs of infection (fever and neutrophilia), and piperacillin-tazobactam was restarted. Eight hours after reintroducing the antibiotic, his platelet count dropped from 317 × 10(3) /mm(3) to 7 × 10(3) /mm(3) . After reviewing all the medications administered to the patient as well as other potential causes of thrombocytopenia, and given the chronology of events, piperacillin-tazobactam was suspected as the most likely offending agent and was therefore replaced by meropenem on postoperative day 17. The patient's platelet count began to rise 2 days after discontinuation of piperacillin-tazobactam and reached 245 × 10(3) /mm(3) by postoperative day 30. No spontaneous bleeding or thrombosis occurred while the patient was thrombocytopenic. Use of the Naranjo Adverse Drug Reaction Probability Scale indicated a probable relationship (score of 6) between the patient's development of thrombocytopenia and piperacillin-tazobactam therapy. This case highlights the severity and swiftness in which drug-induced thrombocytopenia may present in the context of cardiac surgery.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".