Risk assessment for infected endocarditis in <i>Staphylococcus aureus</i> bacteremia patients: When is transesophageal echocardiography needed?
Bibliographic record
Abstract
AIMS: bacteremia (SAB), but a consensus about performing transthoracic echocardiography or transesophageal echocardiography (TEE) as first-line tests is currently lacking. Recently, a new scoring system has been proposed by Palraj et al. to guide the use of TEE in this population. Our aim was to validate this scoring system or modify it, if necessary. METHODS AND RESULTS: Data from SAB patients admitted from 2012 to 2014 were collected. We tested the Palraj scores to stratify patients' risk for endocarditis. Moreover, we analyzed our population to identify any other possible clinical predictors of endocarditis not included in the score. Endocarditis was diagnosed in 38 of 205 patients (18.5%). Palraj's score was effective in the detection of patients at high risk of endocarditis. In addition, we identified the presence of cardiac devices, prolonged bacteremia and intravenous drug abuse (IVDA) as elements strongly correlated with endocarditis. Two scoring systems (Day-1 and Day-5) were derived including IVDA as a variable. Using a Day-1 cut-off value ≥5 and a Day-5 cut-off value ≥2, the 'modified Palraj's score' showed sensitivities of 42.1% and 97.0% and specificities of 88.6% and 32.0% for Day-1 and Day-5 scores, respectively. CONCLUSION: We modify and expand upon an effective scoring system to identify SAB patients at high risk for endocarditis in order to guide use of TEE. The inclusion of IVDA in the criteria for the calculation of the scores improves its effectiveness.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".