Streptococcus agalactiae infective endocarditis in Canada: a multicenter retrospective nested case control analysis
Bibliographic record
Abstract
BACKGROUND: Infective endocarditis (IE) caused by Streptococcus agalactiae (GBS) is increasingly reported and associated with an aggressive course and high mortality rate. Existing literature on GBS IE is limited to case series; we compared the characteristics of patients with GBS IE to patients with GBS bacteremia without IE to identify risk factors for development of IE. METHODS: A nested case-control study in a cohort of adult patients with GBS bacteremia over a 18-year period was conducted across seven centres in three Canadian cities. A chart review identified patients with possible or definite IE (per Modified Duke Criteria) and patients with IE were matched to those without endocarditis in a 1:3 fashion. Multivariate analyses were completed using logistic regression. RESULTS: Of 520 patients with GBS bacteremia, 28 cases of possible or definite IE were identified (5.4%). 68% (19/28) met criteria for definite IE, surgery was performed in 29% (8/28), and the overall in-hospital mortality rate was 29% (8/28). Multivariate analysis demonstrated that IE was associated with injection drug use (OR = 19.6, 95% CI = 3.39-111.11, p = 0.001), prosthetic valve (OR = 11.5, 95% CI = 1.73-76.92, p = 0.011) and lack of identified source of bacteremia (OR = 3.81, 95% CI = 1.24-11.65, p = 0.019). CONCLUSIONS: GBS bacteremia, especially amongst people who inject drugs, those with prosthetic valves, and those with no apparent source of infection, should increase clinical suspicion for IE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".