Absence of infective endocarditis relapse when end-of-treatment fluorodeoxyglucose positron emission tomography/computed tomography is negative
Bibliographic record
Abstract
AIMS: In non-operated infective endocarditis (IE), relapse may impair the outcome of the disease. The aim of the study was to evaluate the relationship between end-of-treatment (EOT) fluorodeoxyglucose positron emission tomography/computed tomography FDG-PET/CT results and relapse in non-operated IE either on native or prosthetic valve. METHODS AND RESULTS: We included 62 patients who underwent an EOT FDG-PET/CT for non-operated IE performed between 30 and 180 days of antibiotic therapy initiation. Qualitative valve assessment categorized initial and EOT FDG-PET/CT as negative or positive. Quantitative analyses were also conducted. Clinical data from medical charts were collected, including endocarditis team decision for IE diagnosis and relapse. Forty-one (66%) patients were male with a median age of 68 years (57; 80) and 42 (68%) had prosthetic valve IE. End-of-treatment FDG-PET/CT was negative in 29 and positive in 33 patients. The proportion of positive scans decreased significantly compared with initial FDG-PET/CT (53% vs. 77%, respectively, P < 0.0001). All relapses (n = 7, 11%) occurred in patients with a positive EOT FDG-PET/CT with a median delay after EOT FDG-PET/CT of 10 days (0; 45). The relapse rate was significantly lower in negative (0/29) than in positive (7/33) EOT FDG-PET/CT (P = 0.01). CONCLUSION: In this series of 62 patients with non-operated IE who underwent EOT FDG-PET/CT, those with a negative scan (almost half of the study population) did not develop IE relapse after a median follow-up of 10 months. These findings need to be confirmed by prospective and larger studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".