Influence of 16S Ribosomal RNA Gene Polymerase Chain Reaction and Sequencing on Antibiotic Management of Bone and Joint Infections
Bibliographic record
Abstract
INTRODUCTION: Amplification of the 16S ribosomal RNA gene by polymerase chain reaction (PCR) followed by analysis of generated sequences can be an important adjunct to conventional cultures. OBJECTIVE: To determine how the results of this approach influence physicians' decisions regarding the management of bone and joint infections. METHOD: Clinical and laboratory findings of patients seen at the Queen Elizabeth II Health Sciences Centre (Halifax, Nova Scotia) between December 2005 and September 2009 were reviewed. Patients who had negative cultures but likely or possible bone and joint infections were further evaluated using 16S rRNA PCR. The impact of the 16S rRNA PCR result on antibiotic management was evaluated and it was assessed whether untreated patients with negative 16S rRNA PCR subsequently presented with infections, suggesting a false-negative result. RESULT: A total of 36 patients (mean age 62 years) were reviewed. Thirty-two patients were evaluated by infectious disease consultants; of these, 20 were considered likely to have infections. Seventeen patients were admitted with suspected prosthetic joint infections. Twenty-nine patients received antimicrobial treatment before the sample for the 16S rRNA PCR assay was obtained. Of the 36 patients, 26 (72.2%) were treated appropriately with modifications to their antibiotic regimen in response to the 16S rRNA PCR assay results. Antimicrobials were discontinued for 19 patients based on negative PCR assay and, in seven patients, antibiotics were changed based on a positive result. There were no relapses among patients with negative PCR assay in whom antibiotics were discontinued. CONCLUSION: 16S ribosomal RNA gene PCR and sequencing is a valuable tool in the guidance of antimicrobial therapy for bone and joint 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 | 0.009 | 0.044 |
| 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 | 0.001 |
| 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".