Empiric Antibiotic Treatment Thresholds for Serious Bacterial Infections: A Scenario-based Survey Study
Bibliographic record
Abstract
BACKGROUND: Physicians face competing demands of maximizing pathogen coverage while minimizing unnecessary use of broad-spectrum antibiotics when managing sepsis. We sought to identify physicians' perceived likelihood of coverage achieved by their usual empiric antibiotic regimen, along with minimum thresholds of coverage they would be willing to accept when managing these patients. METHODS: We conducted a scenario-based survey of internal medicine physicians from across Canada using a 2 × 2 factorial design, varied by infection source (undifferentiated vs genitourinary) and severity (mild vs severe) denoted by the Quick Sequential Organ Failure Assessment (qSOFA) score. For each scenario, participants selected their preferred empiric antibiotic regimen, estimated the likelihood of coverage achieved by that regimen, and considered their minimum threshold of coverage. RESULTS: We had 238 respondents: 87 (36.6%) residents and 151 attending physicians (63.4%). The perceived likelihood of antibiotic coverage and minimum thresholds of coverage (with interquartile range) for each scenario were as follows: (1) severe undifferentiated, 90% (89.5%-95.0%) and 90% (80%-95%), respectively; (2) mild undifferentiated, 89% (80%-95%) and 80% (70%-89.5%); (3) severe genitourinary, 91% (87.3%-95.0%) and 90% (80.0%-90.0%); and (4) mild genitourinary, 90% (81.8%-91.3%) and 80% (71.8%-90%). Illness severity and infectious disease specialty predicted higher thresholds of coverage whereas less clinical experience and lower self-reported prescribing intensity predicted lower thresholds of coverage. CONCLUSIONS: Pathogen coverage of 80% and 90% are physician-acceptable thresholds for managing patients with mild and severe sepsis from bacterial infections. These data may inform clinical guidelines and decision-support tools to improve empiric antibiotic prescribing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".