qSOFA does not predict bacteremia in patients with severe manifestations of sepsis
Bibliographic record
Abstract
BACKGROUND: Bloodstream infections in septic patients may be missed due to preceding antibiotic therapy prior to obtaining blood cultures. We leveraged the FABLED cohort study to determine if the quick Sequential Organ Failure Assessment (qSOFA) score could reliably identify patients at higher risk of bacteremia in patients who may have false negative blood cultures due to previously administered antibiotic therapy. METHODS: We conducted a multi-centre diagnostic study among adult patients with severe manifestations of sepsis. Patients were enrolled in one of seven participating centres between November 2013 and September 2018. All patients from the FABLED cohort had two sets of blood cultures drawn prior to the administration of antimicrobial therapy, as well as additional blood cultures within 4 hours of treatment initiation. Participants were categorized according to qSOFA score, with a score ≥2 being considered positive. RESULTS: Among 325 patients with severe manifestations of sepsis, a positive qSOFA score (defined as a score ≥2) on admission was 58% sensitive (95% CI 48% to 67%) and 41% specific (95% CI 34% to 48%) for predicting bacteremia. Among patients with negative post-antimicrobial blood cultures, a positive qSOFA score was 57% sensitive (95% CI 42% to 70%) and 42% specific (95% CI 35% to 49%) to detect patients who were originally bacteremic prior to the initiation of therapy. CONCLUSIONS: Our results suggest that the qSOFA score cannot be used to identify patients at risk for occult bacteremia due to the administration of antibiotics pre-blood culture.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".