A Case of Severe COVID-19 Pneumonia Diagnosed in Bronchoscopy With Negative Repeated Nasopharyngeal Swabs
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a worldwide pandemic that had emerged in China since December 2019. The disease affects all age groups, with clinical manifestations in the spectrum from asymptomatic to rapidly lethal multi-organ failure, mainly involving the respiratory system. Diagnosis is confirmed mainly by a positive real-time polymerase chain reaction (PCR) nasopharyngeal swab. It is highly recommended to avoid performing invasive procedures in COVID-19 subjects to prevent the potential for dissemination of the pathogen. Treatment consists in particular of respiratory support and symptom relief. Dexamethasone is widely used with encouraging response. There were no cases in the literature that were diagnosed with positive reverse transcription-polymerase chain reaction (RT-PCR) testing only from fluid of involved organs, while repeated nasopharyngeal swabs returned negative for COVID-19. We here describe a case of COVID-19 that presented with moderate-severe pulmonary involvement, diagnosed by RT-PCR testing from broncho-alveolar lavage, while several nasopharyngeal swabs were consistently negative. The patient experienced no improvement under wide-spectrum antibiotics administered initially, and greatly improved after receiving systemic corticosteroids. One can realize from our case that COVID-19 could not be ruled out upon repeated negative RT-PCR nasopharyngeal swabs, and in subjects with highly suspected COVID-19, it is justified to perform invasive procedures, but still using maximal protective measures.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".