Comparison of Self-Collected Oral-Nasal and Mid-turbinate Swabs to Healthcare Worker-Collected Nasopharyngeal Swabs for the Detection of SARS-CoV-2
Bibliographic record
Abstract
Accurate detection of respiratory viruses is essential for infection control, patient management, and public health response. Nasopharyngeal swabs (NPS), collected by healthcare workers (HWC-NPS), remain the gold standard for SARS-CoV-2 detection but require trained personnel and can be uncomfortable for patients. Self-collected swabs, such as oral–nasal swabs (SC-ONS) and mid-turbinate swabs, offer scalable alternatives suitable for mass testing. This study compared the performance of i) SC-ONS, ii) an automation-friendly, redesigned version of mid-turbinate swab (SC-MTS), and ii) HWC-NPS in detecting SARS-CoV-2. Between April and June 2022, paired NPS, ONS, and MTS samples were collected from 100 participants at a COVID-19 assessment centre in Hamilton, ON. Samples were tested for SARS-CoV-2 by RT-PCR. Compared to HWC-NPS, SC-ONS demonstrated 82.1% sensitivity and 100% specificity, while SC-MTS showed 75.0% sensitivity and 100% specificity. Agreement with HWC-NPS was strong for both SC-ONS (κ=0.863) and SC-MTS (κ=0.804). Agreement between SC-ONS and SC-MTS was nearly perfect (κ=0.944). Cellular material yields as well as SARS-CoV-2 viral loads were lower for self-collected swabs than HWC-NPS, while viral load comparisons revealed no significant difference between SC-ONS and SC-MTS. Our results suggest that self-collected ONS and MTS are reliable alternatives to HWC-NPS, offering practical, less invasive, and automation-compatible options for large-scale respiratory virus surveillance and pandemic preparedness.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".