Lung ultrasound may help in the differential diagnosis of suspected oligosymptomatic COVID‐19 patients on hemodialysis: A case report
Bibliographic record
Abstract
INTRODUCTION: Considering that patients on dialysis showed a poor outcome during COVID-19 pandemic, and that COVID-19 symptoms in dialysis patients are often mild or absent, each dialysis unit should implement local strategies to early recognize patients affected by COVID-19. However, many available SARS-CoV-2 diagnostic tests demonstrated a moderate sensitivity, 70%-80% is probably a reasonable estimate. Consequently, having useful tools for differential diagnosis becomes essential. In this scenario, lung ultrasound (LUS) may have an important role in the evaluation of lung involvement in hemodialysis patients during COVID-19 pandemic. METHODS: We present two cases of hemodialysis patients with COVID-19 pneumonia in whom LUS had a central role in the diagnostic process. Ultrasound images of COVID-19 pneumonia show a typical bilateral pattern characterized by multiple or confluent B-lines with spared areas, thickened and irregular pleural line, and rare subpleural consolidations. LUS showed high accuracy in diagnosing COVID-19 pneumonia. FINDINGS: Despite both patients appeared clinically euvolemic and afebrile, they presented with acute diarrhea and oxygen saturation level of 92%-93%. Although clinical manifestations were mild and not specific in both patients, LUS raised suspicion on the possible COVID-19 diagnosis which was confirmed by a positive nasopharyngeal RT-PCR. DISCUSSION: There are many reasons for a patient on dialysis to present shortness of breath, fever, and multiple B-lines at LUS assessment (such as heart failure, fluid overload, vascular access infection, interstitial pneumonia) but the recognition of typical ultrasound patterns of the COVID-19 pneumonia is helpful for differential diagnosis. LUS may have an important role in the screening process of hemodialysis patients during the COVID-19 pandemic, especially in oligosymptomatic patients before the SARS-CoV-2 diagnostic tests, and in those with suspected symptoms and/or known exposure with unexpected negative SARS-CoV-2 diagnostic tests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| 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".