Bibliographic record
Abstract
Since the beginning of the pandemic, molecular methods such as real-time RT-PCR have been used as references for severe acute respiratory syndrome (SARS-CoV-2) detection. With unprecedented demands for SARS-CoV-2 testing, and difficulties acquiring NAAT supplies, clinical laboratories are challenged with providing timely results. Rapid diagnostic tests (RDTs) are simple, rapid, and portable technologies that offer a potential solution to increase the diagnostic testing capacity. Recently, some RDTs have become licensed under emergency use authorization for SARS-CoV-2 detection in the laboratory or point-of-care settings (Food and Drug Administration (FDA), 2020), but despite their high specificity, the applicability of RDTs has been hampered by poor clinical sensitivity, which often falls below the ideal target product profiles recommended by the World Health Organization (Dinnes et al., 2020; World Health Organization (WHO), 2020a; World Health Organization (WHO), 2020b).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.020 |
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".