Computed Tomography Imaging Abnormalities in COVID-19 Mortality Patients
Bibliographic record
Abstract
The gold standard diagnostic testing for coronavirus disease 2019 (COVID-19) is nucleic acid testing (NAT) using reverse transcriptase polymerase chain reaction (RT-PCR). Due to the scarcity of testing resources, chest computed tomography (CT) scans have become critical in diagnosing COVID-19. The aim of this study is to describe the chest CT imaging manifestations of COVID-19 in mortality cases. In this case series, data were collected from 10 mortality patients who tested positive for COVID-19 at a community hospital in Brooklyn, New York. Their basic characteristics, common clinical manifestations and chest CT images were analyzed. Clinical manifestations of the 10 patients included cough, fever, shortness of breath, positive COVID-19 status and elevated inflammatory markers including ferritin, lactate dehydrogenase and C-reactive protein. Chest CT imaging showed ground-glass opacities in all 10 patients, bilateral lung involvement in eight patients, two with patchy consolidations, four with interlobular and intralobular septal thickening, also known as crazy paving pattern, six with diffuse distribution of lesions and one patient had pleural effusion. Chest CT is an important diagnostic tool for COVID-19. The pattern of ground-glass opacities or consolidations in multiple lobes is highly indicative of COVID-19 and could potentially be used as a primary diagnostic tool in the absence of other diagnostic tools such as NAT using RT-PCR. Clin Infect Immun. 2020;5(3):71-76 doi: https://doi.org/10.14740/cii110
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".