Rapid Detection of Methicillin-Resistant Staphylococci from Blood Culture Bottles by Using a Multiplex PCR Assay
Bibliographic record
Abstract
Rapid detection and accurate identification of methicillin-resistant staphylococci are critical for the effective management of infections caused by these organisms. We describe a multiplex PCR-based assay for the direct detection of methicillin-resistant staphylococci from blood culture bottles (BacT/Alert; Organon-Teknika, Durham, N.C.). A simple lysis method followed by a multiplex PCR assay designed to detect the nuc, mecA, and bacterial 16S rRNA genes was performed. A total of 306 blood culture specimens were collected over a period of 10 months from June 1998 to April 1999, consisting of 236 blood cultures growing staphylococci (including 124 methicillin-resistant Staphylococcus spp.), 50 positive blood cultures which grew organisms other than staphylococci, and 20 blood cultures that were negative for bacterial and fungal pathogens after 5 days of incubation and terminal subculture. DNA extraction, PCR, and detection could be completed in 2.5 h. Of the positive blood cultures with staphylococci, the multiplex PCR assay had a sensitivity and specificity of 99.2% and 100%, respectively. Our results show that rapid, direct detection of methicillin-resistant staphylococci is possible, allowing clinicians to make prompt and effective decisions for the management of patients with staphylococcal bacteremia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".