Components of the COPD Assessment Test (CAT) associated with a diagnosis of COPD in a random population sample
Bibliographic record
Abstract
The aim of this study was to determine if components of the COPD Assessment Test (CAT), a validated health status impairment instrument, had additional utility in identifying patients at risk for COPD in whom spirometry testing is appropriate. This study was part of the Canadian Obstructive Lung Disease prevalence study. Consenting participants ≥ 40 years of age were identified by random digit dialing. Smoking history, 8-item CAT scores, and post-bronchodilator spirometry were recorded for each. Stepwise logistic regression analysis was used to identify variables related to the presence of airway obstruction and a final logistic model was developed which best predicted COPD in this sample. Of the 801 individuals approached, 532 were included: 51 (9.6%) had COPD, the majority (92%) of whom fit GOLD I or II severity criteria. Items that correlated significantly with a COPD diagnosis included the CAT total score (p = 0.01) and its breathlessness (p < 0.0001) and phlegm (p = 0.001) components. The final logistic model included: age (<55 or ≥55 years), smoking status (current, former, never) and the CAT breathlessness score (ordinal scale 0-5). The area under the receiver-operating characteristic curve for this model was 0.77, sensitivity was 77.6%, specificity was 64.9% and the positive likelihood ratio was 2.21. In summary, the triad of smoking history, age at least 55 years and the presence of exertional breathlessness were key elements of a simple model which had reliable measurement properties when tested in a random population. This may help identify patients at risk for COPD for whom spirometry testing is recommended.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".