Epidemiology and Predictors of Survival of MERS-CoV Infections in Riyadh Region, 2016
Bibliographic record
Abstract
Background: MERS-CoV emerged as a zoonotic disease in Saudi Arabia with 1437 cases as of July 2016. Objective: This study aimed at describing the epidemiology of MERS-CoV infection, clinical aspects of the disease and the determinants of survival. Methods: Medical records were reviewed between April 2014 and December 2015 to identify admission and discharge with MERS-CoV. Patients’ characteristics, epidemiologic and clinical data and laboratory results were extracted and described. Logistic regression analyses were used to model the determinants of the survival of these patients. Significance of the results were judged at the 5% level. Results: 249 laboratory confirmed cases were admitted mostly in August (20.48%) and September (14.86%) of the year 2015. These cases were non-healthcare workers (85.14%), Saudi (58.63%), males (57.03%) with a mean age of 46.71±17.92 years. A third (39.36%) reported contact with suspected or confirmed cases, developed the disease after 6.2 days and continued to shed the virus for 13.17 days on average. The case fatality rate was 20.08%. The likelihood of being discharged alive was significantly higher among non-Saudi (OR=2.35), healthcare workers (OR=10.822), with no co-morbidities (OR=6.57). Conclusions: MERS-CoV mortality is higher among older patients with severe disease. Further studies are recommended for a better estimation of the incubation period and the period of communicability and the role of animal reservoir.
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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 | 0.000 | 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".