Detecting exacerbations using the Clinical COPD Questionnaire
Bibliographic record
Abstract
BACKGROUND: Early treatment of COPD exacerbations has shown to be important. Despite a non-negligible negative impact on health related quality of life, a large proportion of these episodes is not reported (no change in treatment). Little is known whether (low burden) strategies are able to capture these unreported exacerbations. METHODS: The Clinical COPD Questionnaire (CCQ) is a short questionnaire with great evaluative properties in measuring health status. The current explorative study evaluates the discriminative properties of weekly CCQ assessment in detecting exacerbations. RESULTS: In a multicentre prospective cohort study, 121 patients, age 67.4 ± 10.5 years, FEV1 47.7 ± 18.5% pred were followed for 6 weeks by daily diary card recording and weekly CCQ assessment. Weeks were retrospectively labeled as stable or exacerbation (onset) weeks using the Anthonisen symptom diary-card algorithm. Change in CCQ total scores are significantly higher in exacerbation-onset weeks, 0.35 ± 0.69 compared to -0.04 ± 0.37 in stable weeks (p < 0.001). Performance of the Δ CCQ total score discriminating between stable and exacerbation onset weeks was sufficient (area under the ROC curve 0.75). At a cut off point of 0.2, sensitivity was 62.5 (50.3-73.4), specificity 82.0 (79.3-84.4), and a positive and negative predictive value of 43.5 (35.0-51.0) and 90.8 (87.8-93.5), respectively. Using this cut off point, 22 (out of 38) unreported exacerbations were detected while 39 stable patients would have been false positively 'contacted'. CONCLUSIONS: Weekly CCQ assessment is a promising, low burden method to detect unreported exacerbations. Further research is needed to validate discriminative performance and practical implications of the CCQ in detecting exacerbations in daily care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".