Predictive validity of chronic obstructive pulmonary disease phenotypes in inpatient elective surgery: a population-based study
Bibliographic record
Abstract
INTRODUCTION: Chronic obstructive pulmonary disease (COPD) is prevalent among surgical patients, yet guidance for its preoperative assessment remains limited. Whether previously defined COPD phenotypes influence outcomes after surgery is unknown. METHODS: Population-based retrospective cohort of older adults (≥65 yr) with COPD who underwent inpatient elective surgery in Ontario, Canada. Candidate COPD phenotypes included: advanced COPD with home oxygen; COPD with frailty; COPD with frequent exacerbation; COPD with cardiovascular comorbidity; both asthma and COPD; and COPD alone. Nested Cox proportional hazards models examined the added performance of COPD phenotype when added to a baseline model (age, sex, procedural risk, Surgical Outcome Risk Tool) in predicting survival in the year after surgery using model fit, discrimination, calibration, and net benefit analyses. RESULTS: A total of 116 757 patients with COPD underwent inpatient elective surgery; the most common phenotypes included: COPD alone (41.8%), COPD with cardiovascular comorbidity (31.6%), and COPD with frailty (21.8%). There were significant differences in survival between phenotypes when added to the baseline model: advanced COPD (adjusted hazard ratio [aHR] 5.59) and COPD with frailty (aHR 3.56) were associated with markedly decreased survival, while COPD with frequent exacerbation (aHR 1.45) and COPD with cardiovascular comorbidity (aHR 1.35) were associated with moderately decreased survival vs COPD alone. Addition of COPD phenotype improved model fit (likelihood ratio test P<0.001), discrimination (C-index 0.775 vs 0.720), calibration (integrated calibration index 0.035 vs 0.043), and net benefit across all decision thresholds. CONCLUSION: COPD phenotypes are predictive of postoperative survival and improve perioperative risk stratification. These findings support phenotype-based assessment in the preoperative evaluation of patients with COPD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".