Leveraging polymerase chain reaction technique (GeneXpert) to upscaling testing capacity for SARS-CoV-2 (COVID-19) in Nigeria: a game changer
Bibliographic record
Abstract
According to the Nigeria Centre for Disease Control (NCDC), just above 5,000 people have been tested for the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV-2) infection, as at 9th of April 2020 in Nigeria. A total of 288 confirmed cases, 51 were discharged and seven fatalities were reported. Increasing daily tests are dearly required to end the ongoing silent community transmission of the virus. The polymerase chain reaction (PCR)-based GeneXpert for tuberculosis (TB) diagnosis is now the new hope and a quick win to upscaling the testing capacity. The GeneXpert machines will help to test hundreds of samples daily and provide the results within 24 hours. Larger module of GeneXpert machine should be procured to maximize testing capacity in the major cities, while the “machine-shift” is meticulously coordinated to prevent neglecting tuberculosis testing. We urge NCDC to be transparent about COVID-19 tests data and comprehensive list of GeneXpert testing sites for COVID-19; government should consider extension of the density control measures.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".