Sex-Based Variations in the Clinical Manifestations, Comorbidities, and Outcomes of Patients With COVID-19 in Baghdad, Iraq, 2020
Bibliographic record
Abstract
Background A higher incidence of COVID-19 in males has been widely reported. However, whether clinical manifestations, comorbidities, severity, and outcomes differ between males and females remains an area of active investigation. Objective We aimed to compare the clinical features, comorbidities, severity, and outcomes between male and female patients with COVID-19 from Baghdad, Iraq, in 2020. Methods We performed a records-based cross-sectional study by extracting sociodemographic, clinical manifestation, severity, and outcome data from the records of patients with COVID-19 admitted to 2 COVID-19 hospitals in Baghdad, Iraq, between June and August 2020. Results We reviewed a total of 2111 patient records with a history of COVID-19, and 1175 (55.7%) patients were males. We found that respiratory symptoms, sore throat, and gastrointestinal manifestations were significantly more common among females. In contrast, males had significantly more “other” manifestations. No significant difference was noted for fever, nasal congestion, conjunctival congestion, headache, and musculoskeletal manifestations. Generally, female patients had a significantly higher proportion of comorbidities than males (42.7% vs 36%; P=.002). The proportion of severe and critical cases was not different between males and females. The mean time from diagnosis to the outcome was significantly longer in females (P=.03), but the duration of the hospital stay was not significantly different between males and females. Finally, the case fatality ratio was higher in males (16.1%) than in females (13.2%). Conclusions Sex affects the clinical course and outcomes of patients with COVID-19. Male patients may need more attention, considering the higher case fatality ratio.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".