Optimizing health before elective thoracic surgery: systematic review of modifiable risk factors and opportunities for health services research
Bibliographic record
Abstract
Despite progress in many different domains of surgical care, we are still striving toward practices which will consistently lead to the best care for an increasingly complex surgical population. Thoracic surgical patients, as a group, have multiple medical co-morbidities and are at increased risk for developing complications after surgical intervention. Our healthcare systems have been focused on treating complications as they occur in the hopes of minimizing their impact, as well as aiding in recovery. In recent years there has emerged a body of evidence outlining opportunities to optimize patients and likely prevent or decrease the impact of many complications. The purpose of this review article is to summarize four major domains-optimal pain control, nutritional status, functional fitness, and smoking cessation-all of which can have a substantial impact on the thoracic surgical patient's course in the hospital-as well as to describe opportunities for improvement, and areas for future research efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".