Characteristics of patients with suspected COVID-19 pneumonia and repeatedly negative RT-PCR
Bibliographic record
Abstract
Objectives. Challenges remain and there are still a sufficient number of cases with epidemiological, clinical features and radiological data suggestive of COVID-19 pneumonia that persist negative in their RT-PCR results. The aim of the study was to define the distinguishing characteristics between patients developing a serological response to SARS-CoV-2 and those who did not. Methods. RT-PCR tests used were TaqPath 2019-nCoV Assay Kit v1 (ORF-1ab, N and S genes) from Thermo Fisher Diagnostics and SARS-COV-2 Kit (N and E genes) from Vircell. Serological response was tested using the rapid SARS-CoV2 IgG/IgM Test Cassette from T and D Diagnostics Canada and CMC Medical Devices and Drugs, S.L, CE. Results. In this cross-sectional study, we included a cohort of 52 patients recruited from 31 March 2020 to 23 April 2020. Patients with positive serology had an older average age (73.29) compared to those who were negative (54.82) ( P <0.05). Sat0 2 in 27 of 34 patients with positive serology were below 94% ( P <0.05). There was a frequency of 1.5% negative SARS-CoV-2 RT-PCRs during the study period concurring with 36.7% of positivity. Conclusions. Clinical features and other biomarkers in a context of a positive serology can be considered crucial for diagnosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".