Canadian Public Health Laboratory Network Statement on Point-of-Care Serology Testing in COVID-19
Bibliographic record
Abstract
Key points• It can take at least 7-14 days, and sometimes longer, after symptom onset for antibodies to develop, therefore the use of serology POC tests in the early phase of infection can result in a false negative COVID-19 diagnosis at a time when patients are most infectious (i.e. a negative result does not rule out infection).• False negative interpretations may occur in elderly and immunocompromised patients, who are unable to mount an adequate antibody response.• Since serology POC tests do not detect virus, a positive or negative result does not determine whether a person is infectious.• Positive results may be due to past or recent infection with SARS-CoV-2 or from COVID-19 vaccination.• Most POC serology tests are unable to differentiate antibodies developed from previous infection from those generated in response to COVID-19 vaccination.Given the rapid expansion of COVID-19 vaccination, this further limits the use of serology POC tests.• As with other COVID-19 serological platforms, false positive results may occur if these kits cross-react with antibodies from recent or past exposure to other coronaviruses, including human coronaviruses.• Other infections, as well as non-infectious conditions (e.g.rheumatoid factor-positive diseases), may also cause false positive results.• False positive results are more likely in areas of low prevalence and low vaccine uptake.The local epidemiology and pretest probability of the individual (i.e.clinical and epidemiological risk factors) need to be taken into consideration when interpreting POC serology results.
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 | 0.039 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".