Asthma control and predictive factors among adults in Saudi Arabia: Results from the Epidemiological Study on the Management of Asthma in Asthmatic Middle East Adult Population study
Bibliographic record
Abstract
CONTEXT: Asthma control is suboptimal in the Middle East and North Africa (MENA). AIMS: The aim of this study is to assess the level of asthma control in Saudi patients as per the Global Initiative for Asthma 2012 classification and explore its potential predictive factors. SETTINGS AND DESIGN: Epidemiological Study on the Management of Asthma in Asthmatic Middle East Adult Population (ESMAA) is a multicentric, descriptive, epidemiological study assessing asthma management in the MENA region. In this article, we report the results of patients from Saudi Arabia included in the ESMAA study. METHODS: Adult patients diagnosed with asthma at least 1 year before study entry were considered for inclusion. Asthma control level and its predictive factors were explored. Treatment adherence and quality of life (QoL) were assessed by MMAS-4© and Short Form 8 Health Survey QoL questionnaires, respectively. STATISTICAL ANALYSIS USED: Descriptive statistics were done considering two-sided 95% confidence intervals. Logistic regression was used to explore the potential predictive factors of asthma control. All statistical tests were two-sided, and P < 0.05 was considered statistically significant. RESULTS: Data of 1009 patients from Saudi Arabia were analyzed. Less than one-third of patients (30.1%) were found to have controlled asthma with significantly higher QoL. High level of asthma control was reported among male patients and those with high educational level, while age, body mass index, and adherence to treatment were found to have no effect on asthma control. CONCLUSIONS: Asthma control remains suboptimal among Saudi population. This needs huge efforts to achieve acceptable levels of control and better QoL for asthma patients. Further studies are still needed in Saudi Arabia and the Middle East region.
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 | 0.001 | 0.002 |
| 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".