Comparison of frailty indexes as predictors of clinical outcomes after major thoracic surgery
Bibliographic record
Abstract
Background: Despite greater appreciation for the importance of frailty in surgical patients, due to improved understanding that frailty is often linked to poor outcomes, the optimal method of assessment remains unknown. In this study, we sought to evaluate the prevalence of frailty in patients considered for elective thoracic surgery and to test the ability of several frailty measurements to predict postoperative outcomes. Methods: Patients included were candidates for major elective thoracic surgery. Preoperative assessment of frailty included the Fried frailty phenotype, the Edmonton Frail Scale (EFS), the modified frailty index (mFI), the Clinical Frailty Scale (CFS), and additional components of frailty. Outcome data include days with chest drain, length of hospital stay, and postoperative adverse events. Results: According to the Fried frailty phenotype, 53% of 94 patients included were prefrail or frail. A significant association between frailty and postoperative complications was found (odds ratio 7.65; P=0.001). No association between CFS, mFI, EFS, and complications was observed. The Frailty Phenotype seemed the most accurate in predicting postoperative complications, with an area under the curve (AUC) of 0.77. Twenty-seven percent of patients meet the criteria for depression according to the Geriatric Depression Scale and they showed a higher risk of postoperative complications (OR 2.47; P=0.03). A lower psoas muscle index was associated with a higher risk of complications (OR 3.40; P=0.04). Conclusions: According to our results, the Fried frailty phenotype seems the most accurate tool to test frailty in patients undergoing thoracic resections. Surgeons should be aware that, although these aspects are not routinely tested, they are potential targets to improve clinical outcomes. Studies on additional interventions specifically targeting frail people in the setting of elective thoracic surgery are required.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".