Late Breaking Abstract - Ten-year risk score for COPD: the NHLBI pooled cohorts study
Bibliographic record
Abstract
<b>Background:</b> COPD risk estimation is important to targeting prevention. <b>Aim:</b> Develop a risk score for 10-year risk of incident airflow obstruction. <b>Methods:</b> 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 <i>a priori</i> risk factors (age, sex, race, smoking, packyears, asthma, dyspnea) with/without adjustment for baseline spirometry. <b>Results:</b> 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 <i>a priori</i> risk score but was outperformed by a 9-component score that included the <i>a priori</i> risk factors plus initial FEV<sub>1</sub> and FVC (<b>Figure</b>). <b>Conclusions:</b> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".