COVID-19’s Radiologic, Functional, and Serologic Consequences at 6-Month and 18-Month Follow-up: A Prospective Cohort Study
Bibliographic record
Abstract
Background:We evaluated the radiologic, pulmonary functional, and antibody statuses of coronavirus disease 2019 (COVID-19) patients 6 and 18 months after discharge, comparing changes in status and focusing on risk factors for residual computed tomography (CT) abnormalities.Methods: This prospective cohort study was conducted on COVID-19 patients discharged between April 2020 and January 2021.Chest CT, pulmonary function testing (PFT), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) measurements were performed 6 and 18 months after discharge.We evaluated factors associated with residual CT abnormalities and the correlation between lesion volume in CT (lesion volume ), PFT, and IgG levels.Results: This study included 68 and 42 participants evaluated 6 and 18 months, respectively, after hospitalizations for COVID-19.CT abnormalities were noted in 22 participants (32.4%) at 6 months and 13 participants (31.0%) at 18 months.Lesion volume was significantly lower at 18 months than 6 months (P < 0.001).Patients with CT abnormalities at 6 months showed lower forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC), and patients with CT abnormalities at 18 months exhibited lower FVC.FVC significantly improved between 6 and 18 months of follow-up (all P < 0.0001).SARS-CoV-2 IgG levels were significantly higher in patients with CT abnormalities at 6 and 18 months (P < 0.001).At 18-month follow-up assessments, age was associated with CT abnormalities (odds ratio, 1.17; 95% confidence interval, 1.03-1.32;P = 0.01), and lesion volume showed a positive correlation with IgG level (r = 0.643, P < 0.001).Conclusion: At 18-month follow-up assessments, 31.0% of participants exhibited residual CT abnormalities.Age and higher SARS-CoV-2 IgG levels were significant predictors, and FVC
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".