Molecular Diagnosis of Bacterial Definite Infective Endocarditis by Real-Time Polymerase Chain Reaction
Bibliographic record
Abstract
Infective endocarditis (IE) can be diagnosed using the Duke criteria, which cannot be conclusive especially when the results of blood cultures are negative. This study aimed at using real-time polymerase chain reaction (PCR) technique to isolate bacteria present in whole blood samples of patients with definitive IE on the basis of the method designed in this study. This laboratory and test study was conducted on 20 whole blood samples taken from patients with definitive IE. Real-time PCR of the 16s rRNA was utilized to directly analyze whole blood samples to diagnose bacterial IE. Of 20 whole blood samples with definitive IE, only one blood culture (5%) was positive and the isolated bacterium belonged to Streptococci viridans group. Also, 13 whole blood samples were positive using real-time PCR technique. The isolated bacteria were Enterococcus faecalis with seven (35%) cases, Streptococcus gallolyticus with two (10%) cases, Streptococcus mutans with one (5%) case, Streptococcus sanguinis with one (5%) case, Streptococcus salivarius with one (5%) case, and Staphylococcus aureus with one (5%) case. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) using real-time PCR technique were 65%, 100%, 100%, and 74%, respectively. The developed real-time PCR method allows us to detect bacteria in whole blood samples and is much more sensitive than culturing method. It also permits the differentiation of the main group of bacteria within a few hours for IE. Cardiol Res. 2018;9(2):99-106 doi: https://doi.org/10.14740/cr687w
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".