One-year change in health status and subsequent outcomes in COPD
Bibliographic record
Abstract
BACKGROUND: Poor health status has been associated with morbidity and mortality in patients with COPD. To date, the impact of changes in health status on these outcomes remains unknown. AIMS: To explore the relationship of clinically relevant changes in health status with exacerbation, hospitalisation or death in patients with COPD. METHODS: Characteristics and health status (St George's Respiratory Questionnaire, SGRQ) were assessed over a period of 3 years in 2138 patients with COPD enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study: a longitudinal, prospective, observational study. Associations between change in health status (=4 units in SGRQ score) during year 1 and time to first exacerbation, hospitalisation and death during 2-year follow-up were assessed using Kaplan-Meier plots and log-rank test. RESULTS: 1832 (85.7%) patients (age 63.4±7.0 years, 65.4% male, FEV1 48.7±15.6% predicted) underwent assessment at baseline and 1 year. Compared with those who deteriorated, patients with improved or stable health status in year 1 have a lower likelihood of exacerbation (HR 0.78 (95% CI 0.67 to 0.89), p<0.001 and 0.84 (0.73 to 0.97), p=0.016, respectively), hospitalisation (0.72 (0.58 to 0.90), p=0.004 and 0.77 (0.62 to 0.96), p=0.023, respectively) or dying (0.61 (0.39 to 0.95), p=0.027 and 0.58 (0.37 to 0.92), p=0.019, respectively) during 2-year follow-up. This effect persisted after stratification for age and the number of exacerbations and hospitalisations during the first year of the study. CONCLUSIONS: Patients with stable or improved health status during year 1 of ECLIPSE had a lower likelihood of exacerbation, hospitalisation or dying during 2-year follow-up. Interventions that stabilise and improve health status may also improve outcomes in patients with COPD. TRIAL REGISTRATION NUMBER: NCT00292552, registered at ClinicalTrials.gov.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".