Predictors of long-term mortality after hospitalization for COPD; A Scandinavian multicentre study
Bibliographic record
Abstract
Background: The prognosis of patients hospitalized with chronic obstructive pulmonary disease (COPD) is poor, both in the short and long terms, but the prognosis differs across countries and regions. Several risk factors for mortality have been identified. In many countries, smoking prevalence is now rapidly declining, standards of care for patients with COPD have improved, and decreasing hospitalization rates have been detected. In times of changing epidemiology regarding patients hospitalized for COPD, we wanted to examine the long-term mortality after hospitalization for COPD in Scandinavia. Methods: This work involved a cohort study of 716 patients hospitalized for COPD in 2005 at three Scandinavian hospitals. Long-term mortality was examined, and predictors for mortality were examined with univariate and multivariate analyses. Results: A total of 686 patients were discharged alive. 424 (62%) of the patients died during follow-up. Median survival time was 44.4 months (95% CI 39.2, 49.6). The 2-year mortality was 33%, and 4-year mortality was 52%. Age, previous hospitalizations for COPD, long-term oxygen therapy, heart failure, and thoracic malignancy were significant independent predictors of mortality in a multivariate analysis. Conclusion: Long-term mortality after hospitalization for COPD is high, and older patients with severe COPD, frequent previous hospitalizations, concurrent heart failure, or thoracic malignancy are at particular risk. Patients hospitalized for COPD should have thorough assessment of their treatment and comorbidities to optimize further care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".