Asthma Control Level and Relating Socio-Demographic Factors in Hospital Admissions
Bibliographic record
Abstract
Purpose: Asthma is one of the serious public health problems that we face today and the rate of complete control is very low. This study aims to determine the level of asthma control and its relationship with socio-demographic factors in asthma patients. Methods: This cross-sectional study was conducted between November 2020-April 2021 among people aged 18-64 who applied to the hospital and were not diagnosed with asthma. The data of the research were made with the personal information form, ACT (asthma control test). ACT is a questionnaire consisting of 5 questions. Patients rate each question between one and five points. The total score of the five questions forms the test result. If the total score is 25, it is considered as full control, 24-20 as partial control, and ≤19 as not under control. In the research, 206 people participated. Results: Of the participants, 60.7% were female, 60.2% had a family history of asthma, 94.2% of them used asthma medication, and the average age was 45.7±13.85. In the last 12 years, 50.5% of asthma patients stated that they applied to the emergency department due to respiratory problems, and 23.3% were hospitalized due to these problems. It was determined that 78.6% of asthma patients were not under control, 21.4% were under partial control, and there was no patient under full control. The mean age (48.8) and body mass index (BMI) (29.4) of those whose asthma was not under control were higher than those with partial control (32.2 and 24.7, respectively) (p<0.001). It has been determined that the probability of asthma not being controlled increases as age and BMI increase, and life satisfaction decreases, and it is higher in quit smokers than in current smokers (p<0.05). Conclusion: Asthma is largely uncontrolled. The rate of uncontrolled asthma increases with increasing age and BMI. Patients with high BMI should be supported to lose weight and should be directed to exercise.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".