Bibliographic record
Abstract
While chest x-ray is readily available and may precede RT-PCR test, chest x-ray has low sensitivity early in the COVID-19 disease and shows non-specific lung abnormalities in COVID-19 patients. Chest x-ray is part of the initial diagnostic tool used on COVID-19 patients in some hospitals as it yields fast results compared with reverse transcription-polymerase chain reaction (RT-PCR). Chest Computed Tomography (CT) has been reported to be more sensitive than chest x-ray in determining the presence of COVID-19. Chest x-ray findings in confirmed COVID-19 patients show: Normal lung findings early in the illness and in mildly symptomatic patients Typical ground-glass opacities and consolidation in the lung periphery Lung abnormalities are non-specific and may likewise be present in other infections and coronavirus-types of pneumonia The American College of Radiology (ACR), Center for Disease Control and Prevention (CDC), Canadian Association of Radiologists (CAR), Canadian Society of Thoracic Radiology (CSTR), and British Society of Thoracic Imaging do not recommend the use of chest x-ray to diagnose COVID-19. The Fleisher Society, composed of radiologists and pulmonologists in ten countries, does not recommend a chest x-ray for patients suspected of mild COVID-19. A chest x-ray is recommended for patients with moderate to severe COVID-19 needing immediate triage and patients at high risk for disease progression. Despite presence of chest x-ray findings suggesting COVID-19, RT-PCR test remains the standard diagnostic procedure.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".