Isfahan COVID cohort study
Bibliographic record
Abstract
Background: The Isfahan COVID Cohort (ICC) study was designed to investigate the short- and long-term consequences of patients with COVID-19 in Iran. This report presents the rationale, methodology, and initial results of ICC. Materials and Methods: ICC is a 5-year multicentric prospective cohort study that is ongoing on two groups including 5000 patients hospitalized with moderate or severe and 800 nonhospitalized patients with mild or asymptomatic COVID-19 in Isfahan. The ICC endpoints are morbidity, mortality, incident cases, or worsening of underlying noncommunicable diseases (NCDs) and their risk factors. In the current analysis, we examined the persistent symptoms and incident NCDs or risk factors in 819 previously hospitalized patients who completed 1-year follow-up. Results: The two most common symptoms were joint pain/myalgia (19.7%) and dry cough/dyspnea (18.7%). Around 60% of patients had at least one symptom which was more common among women than men and in middle aged than younger or older patients. Female (odds ratio [OR] =1.88, 95% confidence interval [CI]: 1.39-2.55) and highly-educated patients (OR = 2.18, 95% CI: 1.56-3.04) had higher risk of having any symptom in 1-year follow-up. New cases of hypertension followed by diabetes then coronary heart disease (CHD) were the most common incident NCDs. Conclusion: During 1-year follow-up after hospital discharge, about 60% of patients experienced persistent symptoms. Incident hypertension, diabetes, and CHD were the most common events seen. Close monitoring and extensive health services with integrative approaches are needed to improve the health status of these patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".