Impact of Covid-19 on asthmatic patients in Western region in Saudi Arabia
Bibliographic record
Abstract
Background: Respiratory symptoms are a characteristic feature present in covid-19 patients, and they usually range from mild to severe. Asthma is a chronic disease involving the airways that carry air in and out of the lungs. However, there is limited resources that discuss the relation between asthma and prevalence of COVID-19. Aims: Identify the impact of covid19 on asthmatic patients. Methodology: This is a descriptive cross-sectional study that was conducted to study the impact of COVID-19 on asthmatic patients, which was conducted using a prepared questionnaire which was distributed online among 300 patients with asthma. After collecting the data, MS Excel was used for data entry while SPSS version 24 was used for data analysis. Results: In this study, we were able to collect data from 311 asthmatic patients in response to our questionnaire. Most of the asthmatic patients were females (67.2%) with a ratio of females: males of 2:1. Moreover, most patients thought that they control their asthma well and only 13.5 % indicated that they had frequent emergency visits because of asthma. The prevalence of COVID-19 in asthmatic patients was 64.3 % where a third of patients needed to go to hospital because of their bad condition, 12.6 % needed to be hospitalized in ICU and 56.4 % needed oxygen. Moreover, severity of COVID-19 symptoms and outcomes are related to the control of asthma where better control of asthma was associated with better outcomes including lower need for ICU admission and oxygen need. Conclusion: Prevalence of COVID-19 in asthmatic patients was much higher than the general population especially in female patients aged between 31-40 years old. Moreover, COVID-19 had more severe outcomes in asthmatic patients including higher prevalence of ICU admission and oxygen need. Poorer outcomes of COVID-19 were associated with poor control of asthma. Key words: Asthma, Covid-19, Western Region, Saudi Arabia
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".