Clinical characteristics and in-hospital mortality of COVID-19 adult patients in Saudi Arabia
Bibliographic record
Abstract
OBJECTIVES: To provide a detailed study of demographic, baseline comorbidities, clinical features, and outcome for Coronavirus disease 2019 (COVID-19) patients. METHODS: A record-based case-series study conducted from March 23 to June 15, 2020 in King Saud Medical City, Riyadh, Saudi Arabia. Demographic data, clinical presentation, laboratory investigations, complications, and in-hospital outcome of COVID-19 patients collected with analysis of the clinical characteristics for survivors and deceased. RESULTS: A total of 768 patients were included. The mean age was 46.36±13.7 years and 76.7% were men. Approximately 96.3% reported more than one comorbidity; diabetes mellitus was the most frequent (46.4%). Fever (84.5%), cough (82.3%), and shortness of breath (79.8%) were the main presenting symptoms. During the follow-up, pneumonia reported in 68.6%, acute respiratory distress syndrome in 32.7%, septic shock in 20.7%, respiratory failure in 20.3%, and acute kidney injury in 19.3%. Approximately 45.8% of enrolled patients required intensive care unit admission. Lung disease (odd ratio [OR]=3.862 with 95% confident interval [CI] (2.455-6.074), obesity (OR=3.732, CI=2.511-5.546), smoking (OR=2.991, CI=2.072-4.317), chronic kidney disease (OR=2.296. CI=1.497-3.521), and diabetes mellitus (OR=2.291, CI=1.714-3.063) are predictors of ICU admission. Fatality ratio was 4.27%; therefore, men were more prevalent in dead group. CONCLUSION: Coronavirus disease 2019 places a huge burden on healthcare facilities, particularly in patients with comorbidity. Coronavirus disease 2019 patients who are obese and smokers with history of diabetes mellitus have a high risk of death.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".