Evaluation of the Persian Transcript of the COPD Assessment Test in the Measurement of COPD Health Status in Iranian COPD Patients
Bibliographic record
Abstract
AIM: Chronic obstructive pulmonary disease is a completely irreversible obstructive airway disease. The COPD assessment test (CAT) is one of the standard methods for the clinical assessment of the disease, which is translated into Persian. This study investigated the reliability of the test and its relationship with the severity of the disease. METHODS: In this cross-sectional study, 120 patients filled out the Persian transcript of the test. After two weeks, the patients filled out the CAT test again. Obstruction severity was determined for all the patients using spirometry, and the patients were categorized into four groups according to the Global Initiative for Chronic Obstructive Lung Disease criteria. The relationship between the test scores and the disease severity wan validated. RESULTS: The mean age of the patients was 51.5 years. The Cronbach's alpha coefficient of the Persian transcript of the test was 0.872 in the first time, and 0.885 in the second time. Intragroup reliability, test re-test and intragroup correlations were significant for all the questions (<0.001). The relationship between the test mean score and obstruction severity was significant, and the correlation between disease categorization in accordance with obstruction severity and categorization according to the test score was significant as well. CONCLUSION: The Persian transcript of the assessment test for COPD was reliable and is directly related to the disease severity according to airflow limitation.
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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 | 0.005 | 0.015 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".