Antibiotic-Induced Neutropenia in Patients Receiving Outpatient Parenteral Antibiotic Therapy: a Retrospective Cohort Study
Bibliographic record
Abstract
/L). Patients receiving vancomycin in the OPAT clinic received weekly laboratory monitoring, while those receiving other antibiotics received laboratory monitoring at week 3 of therapy. Out of the 2,513 treatment courses, 55 cases of antibiotic-induced neutropenia were identified, resulting in an incidence of 2.2 cases per 100 treatment courses (95% confidence interval [CI], 1.7 to 2.9). Of the 45 cases for which a sole cause was identified, the three most common intravenous antibiotic culprits were vancomycin (21/541; 3.9%), ceftriaxone (10/490; 2.0%), and cloxacillin (2/103; 1.9%). Five (9.1%) patients had symptoms accompanying neutropenia that warranted hospital admission. There were no deaths, and all patients recovered their neutrophil count after antibiotic discontinuation or completion. In nine cases (16.3%), the culprit beta-lactam antibiotic was changed to another beta-lactam agent containing a structurally different side chain, with successful recovery of the neutrophil count in 9/9 (100%). The highest risk of antibiotic-induced neutropenia was associated with vancomycin, ceftriaxone, and cloxacillin in our cohort. With standardized outpatient monitoring during the third week of OPAT, cases of neutropenia can be detected early and managed without hospitalization. Data from our study also support the safety of switching to alternate beta-lactams with structurally different side chains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".