Approaches for simplified <scp>HCV</scp> diagnostic algorithms
Bibliographic record
Abstract
INTRODUCTION: In the light of the advances in HCV antiviral therapy, global control of HCV infection becomes feasible but depends on the capacity of countries to identify infected people and to offer them treatment. To achieve the WHO goal which targets a diagnosis rate of 90% by 2030, simplification of screening and diagnosis will be crucial. METHODS: Published literature, unpublished data and expert consensus were used to determine key parameters, including point-of-care, rapid diagnostic testing, screening, the use of HCV core Ag and dried blood spots; starting from 2008 until November 2017. In addition, a manual search was undertaken to detect relevant papers or websites related to specific data from countries which underwent or are planning a programme of HCV elimination. RESULTS: Several strategies have been developed and evaluated these last years to simplify and facilitate access to screening and diagnosis, the development of reliable HCV core antigen tests and new nucleic acid amplification technologies for use in decentralized settings. In high prevalence settings, a one-step screening and diagnosis strategy could simplify diagnostic algorithms provided the cost is reduced. Finally, genotyping may no longer be required in the context of availability of pangenotypic antiviral therapy. CONCLUSIONS: Despite relevant advances in HCV screening and diagnosis, the overall diagnosis package is still too expensive today and efforts must be made to allow generalized implementation of reliable tests in low and middle income countries. These efforts will be key factors to foster a real public health approach to HCV elimination.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".