Detection of bacterial pathogens directly from synovial fluids using digital PCR: A proof of concept study
Bibliographic record
Abstract
• A digital PCR assay to detect bacterial pathogens in synovial fluids is presented. • 16S digital PCR can be performed on synovial fluids without prior extraction. • Digital PCR performs as well as real time PCR quickly with less specimen volume. • Digital PCR may be targeted to specific organisms of interest in future studies. Diagnosis of joint infections is often challenging due to low specimen volumes, low sensitivity of Gram stains and long incubation times of cultures. Digital PCR (dPCR) is a molecular tool that can detect nucleic acid targets with high sensitivity and resistance to inhibition. A 3 hour dPCR assay targeting the 16S gene was performed on archived synovial fluids. The assay detected the 16S gene directly from 4 µL of joint fluid without nucleic acid extraction. In 43 culture positive neat synovial fluids, the dPCR instrument detected 31 (72%) as positive, and 12 (28%) as indeterminate. In 49 culture negative specimens, dPCR was negative for 34 (69%), indeterminate for 14 (29%). The detection of bacteria was similar to real-time PCR performed on extracted specimens and demonstrated superior sensitivity to Gram stain. This technique shows potential as a rapid detection method for bacterial pathogens in synovial fluids, with optimization to improve specificity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".