<p>GOLD Classifications, COPD Hospitalization, and All-Cause Mortality in Chronic Obstructive Pulmonary Disease: The HUNT Study</p>
Bibliographic record
Abstract
Purpose: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) has published three classifications of COPD from 2007 to 2017. No studies have investigated the ability of these classifications to predict COPD-related hospitalizations. We aimed to compare the discrimination ability of the GOLD 2007, 2011, and 2017 classifications to predict COPD hospitalization and all-cause mortality. Patients and Methods: We followed 1300 participants with COPD aged ≥ 40 years who participated in the HUNT Study (1995– 1997) through to December 31, 2015. Survival analysis and time-dependent area under receiver operating characteristics curves (AUC) were used to compare the discrimination abilities of the GOLD classifications. Results: Of the 1300 participants, 522 were hospitalized due to COPD and 896 died over 20.4 years of follow-up. In adjusted models, worsening GOLD 2007, GOLD 2011, or GOLD 2017 categories were associated with higher hazards for COPD hospitalization and all-cause mortality, except for the GOLD 2017 classification and all-cause mortality (p trend =0.114). In crude models, the AUCs (95% CI) for the GOLD 2007, GOLD 2011, and GOLD 2017 for COPD hospitalization were 63.1 (58.7– 66.9), 60.9 (56.1– 64.4), and 56.1 (54.0– 58.1), respectively, at 20-years’ follow-up. Corresponding estimates for all-cause mortality were 57.0 (54.8– 59.1), 54.1 (52.1– 56.0), and 52.6 (51.0– 54.3). The differences in AUCs between the GOLD classifications to predict COPD hospitalization and all-cause mortality were constant over the follow-up time. Conclusion: The GOLD 2007 classification was better than the GOLD 2011 and 2017 classifications at predicting COPD hospitalization and all-cause mortality. Keywords: GOLD grades, ABCD groups, COPD hospitalization, mortality, area under curve, AUC
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".