Post-COVID-19 cardio-pulmonary manifestations after 1-year of SARS-CoV-2 infection among Indian population: A single centre, case-control study (OneCoV2 study)
Bibliographic record
Abstract
BACKGROUND: The evolving challenge of persistent symptoms post-Coronavirus disease-2019 (COVID-19), particularly debilitating cardio-pulmonary manifestations, necessitates further exploration. Our study aimed to assess the cardio-pulmonary complications in patients a year after hospital discharge from severe COVID-19, contrasting these with findings from a non-COVID group. METHODS: The OneCoV2 study, a prospective, case-control study, was conducted at a tertiary care teaching hospital in northern India. We enrolled 43 subjects, with a mean age of 25.57 ± 7.94 years (COVID group) and 27.30 ± 8.17 years (non-COVID group). Comprehensive tests included pulmonary function tests, cardiac function tests, 6-min walk tests, and laboratory investigations. RESULTS: Significant differences were found in the pulmonary function [forced vital capacity (FVC) (p = 0.037), forced expiratory flow (FEF) 25-75 % (p = 0.013)], and cardiac function [left ventricular ejection fraction (LVEF) (p = 0.032), heart rate (HR) (p = 0.047)], along with the six-minute walk test results between the two groups. In the COVID group, Pearson's correlation showed a negative correlation between FVC and C-reactive protein (CRP) [r = -0.488, p = 0.007] and a positive correlation between the six-minute walk test [r = 0.431, p = 0.003] and HR [r = 0.503, p = 0.013]. CONCLUSIONS: Our data suggest that pulmonary abnormalities are prevalent in COVID patients even after 1-year of hospital discharge. Cardiac biomarkers also show an inclination towards the COVID group. While we found significant correlations involving some parameters like FVC, CRP, HR, and results from the six-minute walk test, we did not find any significant correlations with the other tested parameters in our study.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".