Impact of Rapid Point-Of-Care Screening Tests for the Identification of Chronic Hepatitis C Infection
Bibliographic record
Abstract
Abstract Background: 170 million persons worldwide are infected with hepatitis C, many of whom are undiagnosed. Although rapid diagnostic tests (RDTs) and point-of-care tests (POCTs) provide a time- and cost-saving alternative to conventional laboratory tests, their global uptake partly depends on their performance. Purpose: To meta-analyze the diagnostic accuracy of POCTs and RDTs to screen for hepatitis C. Data Sources MEDLINE, EMBASE, BIOSIS, and Web of Science (1992 to 2012) and bibliographies of included articles. Study Selection: All studies evaluating the diagnostic accuracy of POCTs and RDTs for hepatitis C in adults (aged 18 years). Data Extraction: Two independent reviewers extracted data and critiqued study quality. Data Synthesis: Of 19 studies reviewed, 18 were meta-analyzed and stratified by specimen type (whole blood, serum, plasma, or oral fluid) or test type (POCT or RDT). Sensitivity was similarly high in POCTs of whole blood (98.9% [95% CI, 94.5% to 99.8%]) and serum or plasma (98.9% [CI, 96.8% to 99.6%]), followed by RDTs of serum or plasma (98.4% [CI, 88.9% to 99.8%]) and POCTs of oral fluid (97.1% [CI, 94.7% to 98.4%]). Specificity was also high in POCTs of whole blood (99.5% [CI, 97.5% to 99.9%]) and serum or plasma (99.7% [CI, 99.3% to 99.9%]), followed by RDTs of serum or plasma (98.6% [CI, 94.9% to 99.6%]) and POCTs of oral fluid (98.2% [CI, 92.2% to 99.6%]). Limitation: Lack of data prevented sensitivity analyses of specific tests. Conclusion: Data suggest that POCTs of blood (serum, plasma, or whole blood) have the highest accuracy, followed by RDTs of serum or plasma and POCTs of oral fluids. Given their accuracy, convenience, and quick turnaround time, RDTs and POCTs may be useful in expanding first-line screening for hepatitis C. Primary Funding Source: Canadian Institutes of Health Research.
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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 | 0.028 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.027 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".