Frailty as a Predictor of Postoperative Morbidity and Mortality Following Liver Resection
Bibliographic record
Abstract
Background Liver resection is commonly performed among patients at risk of being frail. Frailty can be used to assess perioperative risk. Thus, we evaluated frailty as a predictor of postoperative complications following liver resection using a validated modified frailty index (mFI). Methods A retrospective cohort of consecutive patients undergoing liver resection (2011-2018) were stratified according to the mFI and classified as the following: high (≥.27) and low mFI (<.27). The effect of mFI on postoperative complications (Clavien-Dindo) was evaluated using multiple logistic regression, expressed as odds ratios (OR) and 95% CI. Results Of 409 patients, 58 (14%) had high mFI. There were no differences in type of liver resection (laparoscopic: 57% vs 55%, P = .766), number of segments resected (3 vs 4, P = .417), or operative time (257 vs 293 minutes, P = .097) between the high and low mFI groups, respectively. High mFI patients had a longer median length of hospital stay (9.5 vs 5 days, P < .001) and higher proportion of postoperative complications (79% vs 46%, P < .001), including minor complications (69% vs 42%, P < .001), major complications (50% vs 13%, P < .001), and 90-day postoperative mortality (12% vs 3.4%, P = .04). On multivariable analysis, longer operating time (OR 1.15, 95% CI, 1.03 to 1.27), higher number of segments resected (OR 1.43, 95% CI, 1.12 to 1.82), and high mFI (OR 6.74, 95% CI, 2.76 to 16.51) were independent predictors of major postoperative complications. Discussion mFI predicts postoperative outcomes following liver resection and can be used as a risk stratification tool for patients being considered for surgery.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".