Epidural Analgesia Is Associated with Improved Health Outcomes of Surgical Patients with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) have increased postoperative morbidity and mortality. Epidural analgesia (EDA) improves postoperative outcome but may worsen postoperative lung function. It is unknown whether patients with COPD benefit from EDA. The objective of this study was to determine whether patients with COPD undergoing major abdominal surgery benefit from EDA in addition to general anesthesia. METHODS: This cohort study included 541 consecutive patients with COPD who underwent major abdominal surgery between 1995 and 2007 at a university medical center. Propensity scores estimating the probability of receiving EDA were used in multivariate correction. The primary outcome was postoperative pneumonia and 30-day mortality. RESULTS: There were 324 patients (60%) who received EDA in addition to general anesthesia. The incidence of postoperative pneumonia (16% vs. 11%; P = 0.08) and 30-day mortality (9% vs. 5%; P = 0.03) was lower in patients who received EDA. After correction EDA was associated with improved outcome for postoperative pneumonia (OR 0.5; 95% CI: 0.3-0.9; P = 0.03). The strongest preventive effect was seen in patients with the most severe type of COPD. CONCLUSION: This study provides evidence that in patients with COPD who are scheduled for major abdominal surgery, epidural analgesia decreases postoperative pulmonary complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".