Rapid Identification of Bacteria from Positive Blood Cultures by Fluorescence-Based PCR–Single-Strand Conformation Polymorphism Analysis of the 16S rRNA Gene
Bibliographic record
Abstract
Bacteremia continues to result in significant morbidity and mortality, particularly in patients who are immunocompromised. Currently, patients with suspected bacteremia are empirically administered broad-spectrum antibiotics, as definitive diagnosis relies upon the use of blood cultures, which impose significant delays in and limitations to pathogen identification. To address the limitations of growth-based identification, the sequence variability of the 16S rRNA gene of bacteria was targeted for rapid identification of bacterial pathogens isolated directly from blood cultures using a fluorescence-based PCR-single-strand conformation polymorphism (SSCP) protocol. Species-specific SSCP patterns were determined for 25 of the most common bacterial species isolated from blood cultures; these isolates subsequently served as a reference collection for bacterial identification for new cases of bacteremia. A total of 272 blood-culture-positive patient specimens containing bacteria were tested. A previously determined SSCP pattern was observed for 251 (92%) specimens, with 21 (8%) specimens demonstrating SSCP patterns distinct from those in the reference collection. Time to identification from blood culture positivity ranged from 1 to 8 days with biochemical testing, whereas identification by fluorescence-based capillary electrophoresis was obtained as early as 7 h at a calculated cost of $10 (U.S. currency) per specimen when tested in batches of 10. Limitations encountered included the inability to consistently detect mixed cultures as well as some species demonstrating identical SSCP patterns. This method can be applied directly to blood cultures or whole-blood specimens, where early pathogen identification would result in a timely diagnosis with possible implications for patient management costs and the mortality and morbidity of infections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".