HCV reflex testing: A single-sample, low-contamination method that improves the diagnostic efficiency of HCV testing among patients in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) can be cured with antiviral treatments. Diagnosis normally requires two blood samples, one for serology screening and one for molecular confirmation. This multi-step process creates barriers in patient care and decreases testing for hard-to-reach populations. We used the cobas® 6800 to detect HCV RNA after antibody testing to investigate whether a single-sample reflex testing method is effective and efficient for diagnosing HCV-positive patients. METHODS: HCV RNA–positive clinical samples ( n = 152) were interchangeably loaded on the ARCHITECT i2000SR with negative samples ( n = 152) in a checkerboard fashion, tested for HCV antibodies using fixed probes, and directly transferred to the cobas 6800 for molecular testing. Contamination rates, sensitivity, and specificity were determined by comparing Abbott m2000 and cobas 6800 viral loads. After implementing reflex testing, clinical data over a 6-month period were analyzed for diagnostic efficiency. RESULTS: Contamination was present in 5 of 152 pairs (3.29%) after reflex testing. Sensitivity and specificity were 99.3% (95% CI 95.1% to 99.9%) and 100% (95% CI 97.5% to 100%), respectively, using the cobas 6800 assay after serotesting. Approximately 97% of clinical patients received a conclusive test result with the reflex-testing algorithm. For HCV-positive patients, mean diagnostic turnaround times were significantly lower using reflex testing versus the two-sample method (4 versus 39 days; p < 0.0001). CONCLUSIONS: HCV reflex testing demonstrated low levels of contamination without compromising the integrity of the molecular assay. Implementation in clinical laboratories would increase the efficiency of diagnosis and decrease steps in the continuum of care for patients.
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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 | 0.002 | 0.081 |
| 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 | 0.001 |
| 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".