Late Breaking Abstract - Ten-year risk score for COPD: the NHLBI pooled cohorts study
Bibliographic record
Abstract
Background: COPD risk estimation is important to targeting prevention. Aim: Develop a risk score for 10-year risk of incident airflow obstruction. Methods: 6 US general population cohorts that acquired repeated spirometry were harmonized and pooled. Incident airflow obstruction (FEV1/FVC <.70) was defined over the first 10 years of follow-up in adults without baseline airflow obstruction. A risk score without inclusion of spirometry was developed using an Elastic Net procedure in discovery/validation sub-samples. Discriminative performance was evaluated by ROC curve using logistic regression and compared to a risk score including only seven a priori risk factors (age, sex, race, smoking, packyears, asthma, dyspnea) with/without adjustment for baseline spirometry. Results: Of 20,133 participants (mean age 42yrs, 60% female, 67% non-Hispanic White), 733 (4%) developed incident airflow obstruction over 85,338 person-years. In both discovery and validation sub-samples, the 34-component risk score developed by elastic net yielded similar performance compared to the a priori risk score but was outperformed by a 9-component score that included the a priori risk factors plus initial FEV1 and FVC (Figure). Conclusions: A 7-component risk score provides good discrimination of 10-year incident airflow obstruction risk; addition of spirometry substantially improves prediction. A risk score approach could be used to inform spirometry screening guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".