Is self-testing the next paradigm for diagnostics?
Bibliographic record
Abstract
The study estimates the usability and attitude assessment of users for India's first approved rapid antigen self-test kit; the CoviSelf™. India approved its first AI-powered self-test for Covid-19 in April 2021 a few weeks after the first approval in the US. We present here a study on usability and attitude assessment of users of India's first approved rapid antigen self-test kit; the CoviSelf™. The study evaluates participants' understanding of and performance of test procedure and interprets the results. Analysis revealed that more than 90% study participants followed steps correctly as illustrated in the user's manual. Age group and gender-based analysis showed comparable scores for usability of the test kit suggesting users of different age groups has same ease in using the test kit. What we learnt from this study could be start of self-test revolution, where rapid tests could expand the access of diagnostics for hundreds of diseases including HIV, HPV, and dengue to millions of people who could not get access to diagnostics because we lacked manpower or facility to conduct tests. Self-testing could break the barriers for diagnostics that Internet did for information.
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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 | 0.025 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".