P6476Surgery does not appear to improve survival in patients with endocarditis and substance use disorder
Bibliographic record
Abstract
Abstract Background Cohort studies of surgery compared with medical treatment (MT) on endocarditis mortality are conflicting. We conducted a population-based study to estimate associations between treatment and mortality. Methods 1,381 patients with substance use disorder (SUD) and 5,053 without (NSUD) hospitalized for endocarditis were included. Treatment was modeled as a time-dependent variable: patients who underwent surgery after admission were classified as MT until surgery occurred and surgically treated thereafter. Patients without surgery were classified as MT. Adjusted hazard ratios (aHR) between treatment and death (in-hospital, 30 days, one, two, five years) by SUD status were estimated. Results Among SUD patients, there was a trend towards reduction in in-hospital death with surgery vs. MT (aHR 0.61 [95% CI: 0.35–1.04]), but no difference at 30 days (aHR 0.79 [95% CI: 0.42–1.48]). Mortality was higher in SUD patients who underwent surgery compared with MT at one (aHR 1.30 [95% CI: 0.95–1.76]), two (aHR 1.27 [95% CI: 0.97–1.65]), and five years (aHR 1.37 [95% CI: 1.09–1.72]). In NSUD patients, in-hospital mortality (aHR 0.93 [95% CI 0.76–1.16]) did not differ, but 30 day mortality (aHR 1.36 [95% CI 1.04–1.77]) was higher with surgery versus MT, and lower at one (aHR 0.87 [95% CI: 0.73–1.03]), two (aHR 0.75 [95% CI: 0.64–0.88]), and five years (aHR 0.70 [95% CI: 0.61–0.81]). Kaplan-Meier Survival Curves of Patients Interpretation Surgery compared with MT conferred no long-term survival benefit in SUD patients. In NSUD patients, surgery was associated with an initial increased risk of early death followed by a lower risk after one year. Acknowledgement/Funding Grant from Department of Surgery, Queen's University
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".