Comparison of six frailty assessment tools for predicting short-term adverse outcomes in elderly colorectal cancer patients after surgery
Bibliographic record
Abstract
Objective Preoperative frailty in elderly patients with colorectal cancer was evaluated using six frailty assessment tools, frailty phenotype (FP), frailty scale (FRAIL), clinical frailty scale (CFS), Tilburg frailty index (TFI), Edmonton frailty scale (EFS) and Groningen frailty index (GFI). The predictive levels of each scale for short-term adverse outcomes after surgery were compared. Methods A total of 290 elderly patients undergoing laparoscopic radical resection of colorectal cancer in Cancer Hospital Affiliated of Guangxi Medical University from June 2021 to February 2023 were selected as the study subjects. FP, FRAIL, CFS, TFI, EFS, and GFI were used to assess the patient ‘s frailty status. Baseline data and postoperative disability, complications, prolonged hospital stay, and increased treatment costs were collected. The predictive levels of 6 scales for short-term adverse outcomes after surgery were evaluated by receiver operating characteristic (ROC) curves. Results The evaluation of FP, FRAIL, CFS, TFI, EFS and GFI showed that the frailty rate was 41.3%, 29.6%, 38.6%, 65.5%, 37.9% and 38.6%, respectively. There was a statistically significant difference in the detection rates of the six attenuation tools (χ2=88.510, P＜0.01). The maximum AUC for postoperative disability and increased treatment costs were 0.814 and 0.661 predicted by FP. The maximum AUC for postoperative complications and prolonged hospital stay were 0.741 and 0.754 predicted by FRAIL. Conclusion Different frailty tools have significant differences in the detection rate of frailty, with poor consistency; Based on the characteristics and advantages of the tool, taking into account both time and cost, FRAIL is the best predictor of short-term adverse outcomes after 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".