Risk Factors of Cellulitis Treatment Failure with Once-Daily Intravenous Cefazolin Plus Oral Probenecid
Bibliographic record
Abstract
OBJECTIVES: Once-daily intravenous cefazolin with probenecid is used commonly to treat cellulitis. The primary objective of this study was to determine the risk factors of treatment failure with this regimen. METHODS: This was a retrospective cohort study of adult outpatients with cellulitis who were initially treated with once-daily intravenous cefazolin plus probenecid. Treatment failure is defined as inadequate improvement that necessitates either hospital admission or a change in antibiotic therapy to a different intravenous regimen. A stepwise logistic regression analysis was performed to determine the risk factors for regimen failure. RESULTS: From January 2003 to December 2008, 159 patients with cellulitis were initially treated with once daily intravenous cefazolin plus probenecid. Thirty-five (22%) patients had treatment failure. The treatment for 53% (9/17) of the patients with a history of chronic venous disease (CVD) failed, whereas the treatment for 18% (26/142) of patients without CVD failed (P = 0.001). Multivariate analysis identified the presence of CVD as the only risk factor associated with treatment failure (odds ratio 4.4, 95% confidence interval 1.5-13; P = .007). CONCLUSIONS: Patients with cellulitis and CVD who are being treated with once-daily intravenous cefazolin plus probenecid should be monitored closely for treatment failure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".