Clinical study of pulmonary CT lesions and associated bronchiectasis in 115 convalescent patients with novel coronavirus pneumonia (COVID-19) in China
Bibliographic record
Abstract
A total of 115 convalescent inpatients with COVID-19 were enrolled. According to the results of scans of lung lesions via computed tomography (CT), the patients were divided into mild, moderate, and severe groups. The clinical data of the patients were collected, including age, gender, finger pulse oxygen pressure, ventricular rate, body temperature, etc. The correlation between the clinical indicators and the lesions of high-resolution CT (HRCT) and bronchiectasis was analyzed. Among the 115 patients, 82 had no bronchiectasis and 33 had bronchiectasis. The bronchodilation-prone layers mainly included the left and right lower lobe of the lung. The probability of branching in the inflamed area was greater than that in the noninflamed area in patients with COVID-19. There were significant differences in gender, CT lesion range, and number of incidents of bronchiectasis between noninflamed and inflamed areas (P < 0.05). Moreover, there were significant differences in age, total proportion of CT lesions, volume of CT lesions, and total number of patients with bronchiectasis among the three groups (P < 0.05). CT lesion range was positively correlated with the total number of patients with bronchiectasis and patient age (respectively, r = 0.186, P < 0.05; r = 0.029, P < 0.05). The lesion range in HRCT images of lungs in patients with COVID-19 is correlated with bronchodilation. The larger the lesion, the higher the probability of bronchiectasis and the more incidents of bronchiectasis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".