Real-world evaluation of the Lucira Check-It COVID-19 loop-mediated amplification (LAMP) test
Bibliographic record
Abstract
ABSTRACT In hospitals during the COVID-19 pandemic, laboratory testing was important to reduce SARS-CoV-2 transmissions, particularly for high-risk settings like the emergency department and pre-operative settings and for the safe return to work of exposed healthcare workers (HCWs). For these applications, delayed test results from laboratory nucleic acid amplification tests (NAATs) posed a barrier to maximizing efficient patient flow and minimizing staffing shortages. This quality improvement project sought to evaluate the performance of the Lucira Check-It COVID-19 Test, a rapid diagnostic test that used NAAT technology (NAAT-RDT). Using 10-fold serial dilutions of SARS-CoV-2, the analytical sensitivity of the NAAT-RDT was assessed against standard NAATs used for routine diagnostic testing. Clinical performance was assessed at two Nova Scotia hospitals in 405 cases with paired swabs tested by NAAT-RDT and laboratory-based NAATs. These represented three distinct populations: patients presenting to the emergency department ( n = 208), patients in the pre-operative setting ( n = 158), and patients presenting to community testing sites ( n = 38). The analytical sensitivity of the NAAT-RDT and other laboratory NAATs was comparable. During clinical evaluation, the overall sensitivity and specificity were 92.9% and 98.3%, respectively, with little variation between settings. The Lucira NAAT-RDT is a portable and self-contained device that provides an easily interpreted result within 30 minutes following a bilateral nasal swab collection. Its performance was shown to be acceptable for use in three settings in this quality improvement project, facilitating patient flow and management. IMPORTANCE In hospitals during the COVID-19 pandemic, laboratory testing was important to reduce SARS-CoV-2 transmissions, while facilitating patient flow in the emergency department and pre-operative settings, and allowing for the safe return to work of exposed healthcare workers. Delayed test results from laboratory nucleic acid amplification tests (NAATs) posed a barrier to maximizing efficient patient flow and minimizing staffing shortages. This quality improvement project sought to evaluate the analytical and clinical performance of the Lucira Check-It COVID-19 Test, a point-of-care test that used NAAT technology, in the perioperative setting, emergency department, and community testing sites. We found the Lucira Check-It to have comparable performance to laboratory NAATs. It can be employed with little training for specimen collection, processing, and interpretation, and at a cost justifiable from the resources saved from avoiding sample transport and laboratory testing.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".