Noncommunicable disease, clinical course and COVID-19 prognosis: results based on I-CORE Registry
Bibliographic record
Abstract
Background: There are no data on the association between clinical course and comorbidity in Iranian patients with COVID-19. Aims: To determine noncommunicable disease (NCD), clinical characteristics and prognosis of patients hospitalized with COVID-19 in Isfahan, Islamic Republic of Iran. Methods: This multicentric retrospective observational study was performed on all patients hospitalized with COVID-19 in Isfahan from 17 February to 6 April 2020. We recruited 5055 patients. Data on clinical course and comorbid NCDs such as hypertension, coronary heart disease (CHD), diabetes mellitus (DM), cancer, chronic kidney disease (CKD) and chronic respiratory disease (CRD) were collected. Statistical analyses were done by Mann–Whitney U, χ2 and logistic regression tests using Stata version 14. Results: DM and hypertension were the most prevalent comorbidities in patients with positive and negative reverse transcription polymerase chain reaction (RT-PCR). Odds ratio (95% confidence interval) of mortality-associated factors was significant for DM [1.35 (1.07–1.70)], CHD [1.58 (1.26–1.96)], CRD [2.18 (1.58–3.0)], and cancer [3.55 (2.42–5.21)]. These results remained significant for cancer after adjustment for age, sex and clinical factors. Among patients with positive RT-PCR, death was significantly associated with CRD and cancer, while this association disappeared after adjustment for all potential confounders. There was a significant association between NCDs and higher occurrence of low oxygen saturation, mechanical ventilation requirement and intensive care unit admission after adjustment for age and sex. Conclusion: The presence of NCDs alone did not increase mortality in patients with COVID-19, after adjustment for all potential confounders including clinical factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".