Reliable detection of SARS-CoV-2 with patient-collected swabs and saline gargles: A three-headed comparison on multiple molecular platforms
Bibliographic record
Abstract
With increasing demands for SARS-CoV-2 testing, as well as the shortages for testing supplies, collection devices, and trained healthcare workers (HCWs) to collect specimens, self-collection is an attractive prospect to reduce the need for HCWs and expenditure of personal protective equipment. Apart from the traditional nasopharyngeal swab used for SARS-CoV-2 detection, alternative specimens have been validated such as a combined swabs of the oropharynx and anterior nares (OP/N), or throat samples using saline gargles. Both the alternative specimen types are amenable to self-collection. Objectives. This study aimed to compare the sensitivity of HCW-collected (OP/N) swabs, self-collected OP/N swabs, and self-collected saline gargles. Among 38 individuals previously testing positive for SARS-CoV-2 (or their close contacts), two self-collected specimen types (OP/N and saline gargles) were compared to HCW-collected OP/N swabs. SARS-CoV-2 testing was performed on three molecular assays: a laboratory-developed test (LDT), and two commercial assays on automated platforms: Cobas 6800 (Roche Diagnostics) and Panther (Hologic). The sensitivity of self-collected OP/N swabs was equivalent to healthcare worker (HCW)-collected OP/N swabs at 100.0 % [92.6%-100.0%] for all three molecular tests. The sensitivity of saline gargles was not significantly different than HCW-collected OP/N swabs, but varied slightly between instruments at 93.8 % [85.9%-93.8%] for the LDT, 96.8 % [88.6%-96.8%] for the Cobas assay, and 96.7 % [89.2%-96.9%] for the Panther assay. Overall, self-collection using OP/N swabs or saline gargles are reasonable alternatives to HCW-based collections for SARS-CoV-2 detection, and could facilitate broader surveillance strategies.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".