Multiplex Polymerase Chain Reaction and Microcalorimetry in Synovial Fluid: Can Pathogen-based Detection Assays Improve the Diagnosis of Septic Arthritis?
Bibliographic record
Abstract
Objective. To prospectively evaluate automated multiplex PCR and isothermal microcalorimetry tests for rapid and accurate diagnosis of septic arthritis. Methods. Patients with acute arthritis were prospectively included from October 2014 to September 2015. In synovial fluid (SF), leukocyte count and differential, culture, PCR, and microcalorimetry were determined. Septic arthritis was diagnosed by positive SF culture or (1) local clinical signs and symptoms, (2) increased SF leukocyte count, and (3) exclusion of noninfectious causes of inflammatory arthropathy. The performance of individual tests was compared with McNemar’s test. Results. Among 57 patients, 22 (39%) were diagnosed with septic arthritis. SF culture grew a pathogen in 10 patients (46%), PCR was positive in 5 (23%), and microcalorimetry in 10 (46%). Compared to SF culture, 49 concordant pairs were found for both methods (PCR and microcalorimetry; 86% agreement). In SF, PCR failed to detect Staphylococcus aureus (2 patients), Streptococcus pneumoniae (1 patient), Streptococcus dysgalactiae (1 patient), and Clostridium clostridioforme (1 patient). Microcalorimetry failed to detect S. dysgalactiae (1 patient), Streptococcus agalactiae (1 patient), and C. clostridioforme (1 patient). No statistical differences between the performance of SF culture, and PCR and microcalorimetry, respectively, were found. The processing time for PCR was 5 h and for microcalorimetry a median of 8.8 h (range, 2.3–64 h), whereas cultures required a median of 4.5 days (range, 3–14 days). Conclusion. Performance of SF PCR was inferior while microcalorimetry was similar to culture but provided results considerably faster. [Clinical trial registration number ( https://www.clinicaltrials.gov ): NCT02530229 ]
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".