Efficacy of swish and gargle and other collection methods with the use of Abbott ID NOW in COVID-19 detection
Bibliographic record
Abstract
Background: To evaluate the efficacy of the Abbott ID NOW system in detecting COVID-19 using different specimen collection methods, emphasizing diagnostic accuracy and patient comfort. Methods: Three cohorts were analyzed, including two using the nasopharyngeal (NP) swab technique and one utilizing the swish-and-gargle (SG) method. Positive percent agreement (PPA), negative percent agreement (NPA), and cycle threshold (Ct) values were assessed to determine the system's performance. Results: The PPA for the NP swab cohorts averaged around 70%, while the SG cohort exhibited a higher PPA of 80%. All cohorts maintained high NPAs, close to 100%. The SG method significantly reduced false negatives, especially at lower Ct values, indicative of elevated levels of viral RNA. Additionally, the NP swab method, often uncomfortable, posed challenges in repeated testing scenarios, particularly among healthcare workers. Conclusion: While the Abbott ID NOW system demonstrates reliable COVID-19 detection, the SG method emerges as a superior collection technique to NP swabs, offering enhanced diagnostic accuracy and improved comfort for test takers. This study underscores the importance of selecting appropriate collection methods to ensure accurate and efficient COVID-19 testing.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".