Survey of infectious diseases providers reveals variability in duration of antibiotic therapy for the treatment of Gram-negative bloodstream infections
Bibliographic record
Abstract
Abstract Background Trials supporting shorter durations of antibiotic therapy for Gram-negative bloodstream infections (GN-BSI) have recently been published. However, adoption of these findings into practice is unclear given limited eligibility criteria and relatively large non-inferiority margins of these studies. To better understand contemporary management of GN-BSI, we conducted an international survey of infectious diseases (ID) specialists. Methods We developed and disseminated an online survey to assess practice patterns involving treatment duration of GN-BSI, including providers from 28 countries. χ2 tests, t-tests and multivariable linear regression with generalized estimating equations were used to identify factors associated with treatment duration. Results In total, 277 ID specialists completed the survey (64% physicians, 31% pharmacists). The median reported duration of antibiotics was 7 days (IQR, 7–10 days) for all GN-BSI sources. Thirty percent of providers typically recommend durations that differ by ≥7 days depending on the source of GN-BSI, and 71% treat ≥10 days for at least one source. In an adjusted model, factors associated with increased duration included intra-abdominal (+1.01 days, 95% CI 0.57–1.45 days; P < 0.0001), vascular catheter (+0.74 days; 0.33–1.15 days; P = 0.0004), and respiratory (+0.76 days; 0.38–1.14 days; P < 0.0001) sources of GN-BSI relative to urinary sources. Providers that transition patients to oral therapy report shorter durations than those who treat with full IV therapy (−0.60 days; −1.12 to −0.09 days; P = 0.02). Conclusions There is extensive heterogeneity in duration of therapy for treating GN-BSI, particularly with respect to source of GN-BSI. Investigations into appropriate treatment durations for different GN-BSI sources are needed.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".