Evaluation of the rational prescription of linezolid, the prevalence of thrombocytopenia and major drug interactions in patients with cardiovascular diseases: are there any cautions?
Bibliographic record
Abstract
The present study evaluated the rational prescription of linezolid, the prevalence of thrombocytopenia, and major drug interactions in patients with cardiovascular diseases. We conducted a retrospective cross-sectional study on linezolid-treated patients at Shahid Chamran Heart Hospital in Isfahan from March 21, 2021, to March 20, 2022. Our research involved 132 patients who received linezolid. We reported 43.18% of linezolid prescriptions as irrational. Linezolid-induced thrombocytopenia is more common than previous studies, with a prevalence of 47.9%. We found a significant relationship between thrombocytopenia and the concomitant use of aspirin. The duration of treatment was identified as predicting factor for linezolid-induced thrombocytopenia. Moreover, the prevalence of interactions in the X and D categories was determined. Serotonergic and catecholamine medications were associated with 56.1% and 47.7% medication interactions, respectively. Our study found a high prevalence of linezolid-induced thrombocytopenia among patients with cardiovascular diseases. Based on this study, physicians should focus more closely on prescribing linezolid to patients with cardiovascular diseases. In addition to following rational antibiotic use, this susceptible group is also at an elevated risk of side effects (thrombocytopenia) and medication interactions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".