Bibliographic record
Abstract
Thrombocytopenia is a relatively common laboratory finding in the intensive care unit (ICU) that may be caused by a variety of triggers. Vancomycin may lead to the production of antibodies that could precipitate a severe drop in platelet count. The objective of the current report was to describe the development of severe thrombocytopenia during the infectious work-up of a critical care patient, where an association between vancomycin and thrombocytopenia was unexpectedly identified. The current report describes the case of a 64-year-old female who presented with dyspnea on exertion and resting hypoxia to an outside facility and was initially treated for a chronic obstructive pulmonary disease exacerbation. She was then transferred to Mayo Clinic for further care. After 72 h, she acutely decompensated requiring increasing oxygen support with a questionable infiltrate on chest imaging and was transferred to the ICU. She was placed on non-invasive ventilation with poor gas exchange and ultimately required intubation. Antibiotics were expanded to include vancomycin. Within 72 h of antibiotic initiation, her platelet count decreased from 379 х 10 9 /L to 7 х 10 9 /L. After a negative heparin-induced thrombocytopenia panel and serotonin assay, the antibiotics were discontinued and her platelet count subsequently increased. Despite the fact that heparin is a common culprit of drug-induced thrombocytopenia, the patient had no evidence of this association. She received three doses of vancomycin prior to the recognition of the association between this medication and thrombocytopenia. Her platelet count only started to recover once vancomycin was discontinued. In conclusion, vancomycin is commonly administered in the critical care setting and may lead to the development of thrombocytopenia. Vancomycin-induced thrombocytopenia is caused by antibodies that may remain for years after last exposure. Appreciation of the association between vancomycin and thrombocytopenia may ultimately lead to improved patient outcomes. J Med Cases. 2013;4(12):792-795 doi: https://doi.org/10.4021/jmc1555w
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".