Comparison of the Clinical Course of COVID-19 Pneumonia and Acute Respiratory Distress Syndrome in 2 Passengers from the Cruise Ship Diamond Princess in February 2020
Bibliographic record
Abstract
BACKGROUND Patients with coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 can rapidly progress to acute respiratory distress syndrome (ARDS). Because clinical diagnosis of ARDS includes several diseases, understanding the characteristics of COVID-19-related ARDS is necessary for precise treatment. We report 2 patients with ARDS due to COVID-19-associated pneumonia. CASE REPORT Case 1 involved a 72-year-old Japanese man who presented with respiratory distress and fever. Computed tomography (CT) revealed subpleural ground-glass opacities (GGOs) and consolidation. Six days after symptom onset, reverse transcription-polymerase chain reaction (RT-PCR) testing confirmed the diagnosis of COVID-19-associated pneumonia. He was intubated and received veno-venous extracorporeal membrane oxygenation (ECMO) 8 days after symptom onset. Follow-up CT revealed large diffuse areas with a crazy-paving pattern and consolidation, which indicated progression of COVID-19-associated pneumonia. Following treatment with antiviral medications and supportive measures, the patient was weaned off ECMO after 20 days. Case 2 involved a 70-year-old Asian man residing in Canada who presented with cough, malaise, nausea, vomiting, and fever. COVID-19-associated pneumonia was diagnosed based on a positive result from RT-PCR testing. The patient was then transferred to the intensive care unit and intubated 8 days after symptom onset. Follow-up CT showed that while the initial subpleural GGOs had improved, diffuse GGOs appeared, similar to those observed upon diffuse alveolar damage. He was administered systemic steroid therapy for ARDS and extubated after 6 days. CONCLUSIONS Because the pattern of symptom exacerbation in COVID-19-associated pneumonia cases seems inconsistent, individual treatment management, especially the CT-based treatment strategy, is crucial.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".