Infective endocarditis in patients with pyogenic spondylodiscitis: implications for diagnosis and therapy
Bibliographic record
Abstract
OBJECTIVEThe incidence of patients with pyogenic spinal infection is increasing. In addition to treatment of the spinal infection, early diagnosis of and therapy for coexisting infections, especially infective endocarditis (IE), is an important issue. The aim of this study was to evaluate the proportion of coexisting IE and the value of routine transesophageal echocardiography (TEE) in the management of these patients.METHODSThe medical history, laboratory data, radiographic findings, treatment modalities, and results of TEE of patients admitted between 2007 and 2017 were analyzed.RESULTSDuring the abovementioned period, 110 of 255 total patients underwent TEE for detection of IE. The detection rate of IE between those patients undergoing and not undergoing TEE was 33% and 3%, respectively (p < 0.0001). Thirty-six percent of patients with IE needed cardiac surgical intervention because of severe valve destruction. Chronic renal failure, heart failure, septic condition at admission, and preexisting heart condition were significantly associated with coexisting IE. The mortality rate in patients with IE was significantly higher than in patients without IE (22% vs 3%, p = 0.002).CONCLUSIONSTEE should be performed routinely in all patients with spondylodiscitis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".