Risk factors for postoperative pulmonary complications in coronary artery bypass graft surgery patients
Bibliographic record
Abstract
BACKGROUND: Despite numerous advances in anesthesia, surgical techniques, and postoperative care for coronary artery bypass graft (CABG) surgery, postoperative pulmonary complications (PPCs) still account for postoperative morbidity. OBJECTIVE: To determine current risk factors for PPCs in CABG surgery patients. METHODS: A retrospective cohort design was used. Health records were reviewed for patients (n=315) who had CABG surgery at a large quaternary healthcare center over a 4 month period. Pre-, peri-, and postoperative risk factors for PPCs were recorded as binary variables. Data were further assessed according to PPCs and non-PPCs using logistic regression models. RESULTS: PPCs occurred in 99.4% of this CABG surgical cohort. Atelectasis, pleural effusion, atelectasis with pleural effusion, and pneumonia were the most frequent PPCs post CABG surgery. Age >65 years, diabetes, and ASA classification >3 were found to be related to the presence of atelectasis. No significant risk factors were related to the development of pleural effusion or atelectasis with pleural effusion. Postoperative pneumonia was associated with previous myocardial infarction, ventilation >10 h, and hospital stay >5 days. History of bronchitis and COPD were related to postoperative pneumothorax; history of heart failure, COPD, and other lung diseases were related to postoperative pulmonary edema. CONCLUSION: These findings contribute to the understanding of PPCs in post-CABG surgery patients and assist in identification of patients at risk for developing PPCs.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".