Can the Use of Bedside Lung Ultrasound Reduce Transmission Rates in The Case of The COVID-19 Patient? - A Narrative Review
Bibliographic record
Abstract
Novel Corona Virus Disease-19 (nCov-19, COVID-19) was recognised as a pandemic by the World Health Organization on March 11, 2020. As of June 14, 2020, this contagious viral disease has afflicted 188 out of 195 countries in the world with 7,893,700 confirmed cases and 432,922 global deaths.Canada has 98,787 people infected and 8,146 deaths. COVID-19 is thought to transmit through contact, droplets and aerosolization. A rapid review showed limited information on the benefits of conducting lung ultrasound (LUS) versus chest radiograph (CXR) or studies correlating lung ultrasound to chest computed Tomography (CT) in patients positive for Covid-19. The literature review confirmed that CT and LUS cannot diagnose this disease, but that both can help in the management and staging of this disease. There is no literature to prove that LUS at the bedside may be beneficial from the view of decreased transmission to other health care workers and bystanders due to reduced transit but comparing the transit pathway and contact leads one to propose that this would be so. Pregnant patients with COVID-19, young children and patients in the reproductive stage would also benefit from LUS since there is no radiation dose and the critical patient in distress will benefit from testing at the bedside.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".