Sex Differences in the Relation between Comorbidities and Prognosis in Hospitalized Patients with COVID-19
Bibliographic record
Abstract
Purpose: There is a lack of information of the difference in sex-aggregated prevalence of comorbid noncommunicable disease (NCD) in patients hospitalized with COVID-19 in Iran. This study aimed to evaluate sex differences in the relation between medical comorbidities and subsequent death in patients hospitalized with COVID-19. Methods: , 2020, in Isfahan, Iran, were recruited in the ongoing I-CORE Registry. Real-time reverse-transcription polymerase chain reaction (RT-PCR) testing was done upon admission. Data on preexisting comorbid NCDs including hypertension, coronary heart disease (CHD), diabetes mellitus (DM), cancers, chronic renal disease (CRD), and chronic respiratory disease were collected through self-reported questionnaires. Results: Overall, 12,620 individuals were enrolled in this registry of which 4,356 were positive for the COVID-19 RT-PCR test. In the whole population, in women, DM, hypertension, and CHD, and in men, DM, CHD, and hypertension were, respectively, the most frequent comorbidities. The frequency of at least one NCD did not differ between men and women, but a greater proportion of women had two or more NCDs. Increasing the number of comorbidities was associated with higher death frequency and mortality risk in the unadjusted model but remained no longer significant after adjustment for age. There was no statistically significant difference in this regard between men and women. Conclusion: Overall, we found that DM, hypertension, and CHD were the most frequent comorbidities. Although comorbidities were more frequent among women, mortality risk did not significantly differ between men and women.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".