Between-country variability in prognostic properties of exacerbation history in COPD
Bibliographic record
Abstract
BACKGROUND: In COPD management, risk stratification for future exacerbations relies on exacerbation history. A rational management strategy should advocate the same treatment for people at the same risk. Our aim was to assess post-test probability of exacerbation history in a range of countries and examine its utility as a prognostic marker. METHODS: In NOVELTY, a 3-year international observational study of patients with asthma and/or COPD, we determined exacerbation risk (the occurrence of at least one moderate/severe event) during the first follow-up year (pretest probability), and the sensitivity and specificity of exacerbation history (with a high-risk definition of ≥2 moderate or ≥1 severe events in previous 12 months) by country. We applied Bayes’ rule to calculate the post-test probability of events, and Pauker and Kessler’s threshold approach for clinical usefulness, examining post-test probability thresholds of 10%, 25%, and 50%. RESULTS: NOVELTY included 3,874 COPD patients (mean age 66.7 years, 38.4% female) in 18 countries. Pre-test probability ranged from 7.1% to 51.0%. Positive post-test probability was 14.7% to 62.0% (Figure). At a risk threshold of 10%, exacerbation history was a useful test only in 2 countries. At 25%, it was useful in 8 countries, and at 50% in 2 countries. CONCLUSION: COPD exacerbation history carries widely varying prognostic information that could result in inconsistent treatment decisions. erj;66/suppl_69/PA2018/F1 F1 F1
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".