Opportunities and Challenges for Cost-Efficient Implementation of New Point-of-Care Diagnostics for HIV and Tuberculosis
Bibliographic record
Abstract
Stakeholders agree that supporting high-quality diagnostics is essential if we are to continue to make strides in the fight against human immunodeficiency virus (HIV) and tuberculosis. Despite the need to strengthen existing laboratory infrastructure, which includes expanding and developing new laboratories, there are clear diagnostic needs where conventional laboratory support is insufficient. Regarding HIV, rapid point-of-care (POC) testing for initial HIV diagnosis has been successful, but several needs remain. For tuberculosis, several new diagnostic tests have recently been endorsed by the World Health Organization, but a POC test remains elusive. Human immunodeficiency virus and tuberculosis are coendemic in many high prevalence locations, making parallel diagnosis of these conditions an important consideration. Despite its clear advantages, POC testing has important limitations, and laboratory-based testing will continue to be an important component of future diagnostic networks. Ideally, a strategic deployment plan should be used to define where and how POC technologies can be most efficiently and cost effectively integrated into diagnostic algorithms and existing test networks prior to widespread scale-up. In this fashion, the global community can best harness the tremendous capacity of novel diagnostics in fighting these 2 scourges.
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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 | 0.036 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".