Direct Clinical Evidence Recommending the Use of Proteinase K or Dithiothreitol to Pretreat Sputum for Detection of SARS-CoV-2
Bibliographic record
Abstract
One of the primary tools for diagnosing COVID-19 is the nucleic acid-based real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test performed on respiratory specimens. The detection rate of SARS-CoV-2 in lower respiratory specimens (such as sputum) is higher than that for upper respiratory specimens (such as nasal and pharyngeal swabs). However, sputum specimens are usually quite viscous, requiring a homogenization process prior to nucleic acid (NA) extraction for RT-PCR. Sputum specimens from COVID-19 and non-COVID-19 patients were treated with four commonly used reagents-saline, N-acetyl-L-cysteine (NALC), proteinase K (PK), and dithiothreitol (DTT), prior to NA extraction. These reagents were then compared for their performance in diagnosing COVID-19 in real clinical practice. The detection rate of SARS-CoV-2 in PK- or DTT-treated sputum was comparable, and higher than that in sputum treated with NALC or saline. While there was a 4.8% (1/21) false negative rate for the PK- and DTT-treated sputum, neither treatment showed any false positive cases among patients with non-COVID diseases. Moreover, sputum pretreated with saline, NALC, PK or DTT showed higher detection rates of SARS-CoV-2 as compared to pharyngeal swabs. Taken together, we provide direct evidence recommending the use of PK or DTT to pretreat sputum samples to facilitate SARS-CoV-2 detection by clinical laboratories. Moreover, our methods should help to standardize the procedure of processing sputum specimens and improve the ability to detect SARS-CoV-2 in these samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".