Cross-Sectional Study of Chronic Obstructive Pulmonary Disease Prevalence Among Smokers, Ex-Smokers, and Never-Smokers in Almaty, Kazakhstan: Study Protocol
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is significantly underdiagnosed in Kazakhstan, and there is no previously conducted study on COPD prevalence in the country. OBJECTIVE: The purpose of this study is to assess the prevalence of COPD among individuals aged 40 to 59 years based on results of spirometry before and after bronchodilator, presence of structural changes in the lungs (emphysema, inflammatory changes, and thickening of the walls of the large and small airways) detected by computer tomography, and the symptoms of COPD. The study has 3 study groups: smokers of conventional cigarettes, those who had quit smoking 1 to 5 years ago, and those who haven't smoked cigarettes. METHODS: This is an observational study with a cross-sectional design among individuals aged 40 to 59 years in Almaty, Kazakhstan. The sample of 900 individuals of both sexes contains 500 smokers, 200 ex-smokers, and 200 never-smokers. Study measures include spirometry, chest computed tomography, electrocardiography, physical exams, laboratory testing of serum, anthropometry, and 6-minute walk test. Data are collected by computer-assisted personal interviewing with tablets. The questionnaire was designed to explore possible COPD risk factors including history of smoking, current smoking, level of smoking exposure (in pack-years), passive smoking, occupational and environmental hazards, and covariates: age, gender, ethnicity, education, occupation, and self-reported morbidity. COPD Assessment Test (CAT) is used to collect information about COPD symptoms. RESULTS: We have completed the participant recruitment and study procedures. Currently, we are working on data processing and data analysis. The authors anticipate the preliminary results should be available by September 2017. Study results will be published in peer-reviewed scientific journals. CONCLUSIONS: This is the first study in Kazakhstan that assesses prevalence of COPD and its comorbidities in the adult population aged 40 to 59 years. The results of the study will be useful for improving COPD preventive measures, better COPD screening, identification, and registration. Findings of the study will also contribute to global knowledge on the epidemiology of COPD. TRIAL REGISTRATION: ClinicalTrials.gov NCT02926534; https://clinicaltrials.gov/ct2/show/NCT02926534 (Archived by WebCite at http://www.webcitation.org/6rjwGsPOZ).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".