Canadian Antibiotic Prescribing for Sepsis (CAPS) study: A <i>post hoc</i> analysis of the FABLED cohort study
Bibliographic record
Abstract
Background: Understanding the microbiology and optimal pharmacotherapy of patients with community-onset sepsis is key to improving outcomes. Yet, empiric therapies prescribed in Canadian emergency departments as they relate to microbial etiology and focus of infection are inadequately described. Methods: analysis of the FABLED cohort study, which quantified the effect of antimicrobials on blood culture yield in septic patients. Patients presenting with sepsis were enrolled in six Canadian emergency departments between 2013 and 2018. We characterized the appropriateness of empiric therapies relative to the pathogens isolated and focus of infection identified. Results: was never isolated in any blood cultures, and drug-resistant organisms were only encountered in 4.8% of the cohort. Among patients with bacteremia (n = 100), 28% of patients received appropriate antibiotic therapy whereas the remainder received therapies that were either overly narrow (16%) or unnecessarily broad (56%) in spectrum. Among patients with an identified focus of infection (n = 266), 30.5% received appropriate empiric antibiotics. Prescribing patterns that were overly broad, overly narrow, or a combination of the two were observed in 39.8%, 7.5%, and 22.2% of patients, respectively. Thirty-day mortality was lowest among patients receiving appropriate therapy relative to the final pathogen isolated and presumed infectious focus. Conclusions: Empiric therapies for septic patients in Canada were overly broad given the rare isolation of drug-resistant pathogens. Though likely confounded by severity of illness, optimal outcomes were observed when therapy was appropriate relative to the causative pathogen and infectious focus.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".