Diagnosis and Direct Automated Sequencing of HIV-1 From Dried Blood Spots (DBS) Collected on Filter Paper
Bibliographic record
Abstract
Since its discovery in 1981, human immunodeficiency virus type 1 (HIV-1) has rapidly emerged as one of the most devastating infectious pathogens of this century (1-3). The World Health Organization (WHO) estimates that, as of 1995, there were at least 15 million HIV- infected men, women, and children worldwide, with the vast majority of infections occurring in developing countries and isolated rural regions where specimen collection, preparation and shipment are difficult (4). Simple and improved sampling methods that can be widely applied under difficult field conditions are needed to effectively monitor the changing dynamics of the HIV-1/AIDS pandemic, track the spread of HIV-1 variants among different population groups, and ensure that research and interventive activities are directed against biologically important variants of the virus. To date, at least eight major HIV-1 subtypes, designated A through H, have been identified (5,6). More recently, a ninth subtype, I, has been detected (7), as well as several highly divergent, or "outlying" variants of HIV-1 that have been tentatively classified as group O (8,9). This subtyping is based on a relatively small number of specimens collected from a few geographic areas and the full range and distribution of HIV-1 variants remains to be established. The collection of whole blood on filter paper provides an innovative and powerful approach for the systematic and unbiased collection of large numbers of field specimens for diagnostic and surveillance purposes (10-19).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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".