Characteristics of patients with suspected COVID-19 pneumonia and repeatedly negative RT-PCR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it