The importance of lung ultrasound in the diagnosis of COVID-19
Bibliographic record
Abstract
StreSZcZenIeW artykule omówiono rolę badania ultrasonograficznego (USG) w postępowaniu u pacjentów zakażonych wirusem SARS-CoV-2.Głównym celem była odpowiedź na pytanie, czy znaczenie USG jest na tyle duże, aby ta technika mogło częściowo zastępować badania wykorzystujące promieniowanie jonizujące.Tomografia komputerowa uznawana za złoty standard w badaniach płuc lub też często wybierane badanie rentgenowskie wiążą się z transportem zakażonych pacjentów i narażaniem przy tym personelu.Metoda USG pozwala na stosunkowo szybkie, łatwo dostępne i nieinwazyjne badanie przyłóżkowe pacjenta, które w wystarczającym stopniu ukazuje charakter i rozległość uszkodzenia płuc, co jest najważniejsze do podjęcia pierwszych kroków leczenia danego pacjenta.Słowa klucZowe COVID-19, śródmiąższowe zapalenie płuc, badanie ultrasonograficzne płuc, badanie rentgenograficzne klatki piersiowej, tomografia komputerowa klatki piersiowej.abStract This review is focused on the role of ultrasound examination in the management of virus-infected patients.The main aim of the study is to answer the question: is the importance of ultrasound high enough to at least partially replace the examinations using ionizing radiation?Computed tomography, considered the gold standard in lung examinations, or the frequently chosen X-ray method, involves transporting infected patients and putting staff at risk.The USG method allows a relatively quick, easily accessible and non-invasive bedside examination of the patient, which sufficiently shows the nature and extent of lung damage, which is the most crucial for taking the first steps of treatment for a given patient.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".