Association between Antifungal Prophylaxis and Rate of Documented Bacteremia in Febrile Neutropenic Cancer Patients
Bibliographic record
Abstract
Published data have suggested a correlation between antifungal prophylaxis and bacteremia in febrile neutropenia. This correlation was investigated among 3002 febrile neutropenic patients enrolled in 4 trials during 1986-1994. Globally, 1322 patients (44%) did not receive antifungal prophylaxis; 835 (28%) received poorly absorbable antifungal agents and 845 (28%) received absorbable antifungal agents. The rates of bacteremia for these groups were 20%, 26%, and 27%, respectively (P=.0001). In a multivariate model without including antifungal prophylaxis, factors associated with bacteremia were: age, duration of hospitalization, duration of neutropenia before enrollment, underlying disease, presence of an intravenous catheter, shock, antibacterial prophylaxis, temperature, and granulocyte count at onset of fever. When antifungal prophylaxis was included, the adjustment quality of the model improved slightly (P=.05), with an odds ratio of 1.19 (95% confidence interval [CI], 0.92-1.55) for patients receiving nonabsorbable and 1.42 (95% CI, 1.07-1.88) for those who were receiving absorbable antifungal agents. Antifungal prophylaxis with absorbable agents might have an impact on the rate of documented bacteremia in febrile neutropenia. This effect should be confirmed prospectively.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".