Application value of different frailty assessment tools in older patients undergoing major abdominal surgery
Bibliographic record
Abstract
BACKGROUND: Multiple frailty assessment tools are available for clinical practice, but the optimal tool remains unclear. This study aimed to compare the diagnostic performance of frail scale (FS), frailty phenotype (FP),11-item modified frailty index (mFI-11), Edmonton Frail Scale (EFS), and Tilburg Frailty Indicator (TFI) for frailty taking the comprehensive geriatric assessment (CGA) as the gold standard, and their ability to predict 30-day postoperative complications and prolonged length of stay (PLOS). METHODS: This study recruited older patients (≥ 65 years) undergoing elective major abdominal surgery. The receiver operating characteristic (ROC) curves, technique for order preference by similarity to ideal solution (TOPSIS), and decision analysis curve (DCA) were used to validate the diagnostic, comprehensive, and predictive performance of 5 tools in frailty, complications, and PLOS. RESULTS: EFS presented moderate consistency with CGA (Kappa = 0.544, P < 0.001), excellent performance in diagnosing frailty (area under the ROC curve (AUC) = 0.881, P < 0.001), and high clinical net benefit within the risk threshold ranging from 0.8 % to 57.44 %. Although EFS had the largest AUC for predicting complications (AUC = 0.612) and PLOS (AUC = 0.642) and showed high clinical net benefit, its predictive performance was poor (AUC < 0.7). The TOPSIS indicated that EFS required optimization in multiple aspects (closeness coefficient (Ci) < 0.8). CONCLUSION: EFS has excellent diagnostic performance and clinical net benefit for frailty. However, further research is required to identify optimal tools or combine EFS with additional indicators to enhance its comprehensive and predictive performance for complications and PLOS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| 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) | 0.001 | 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".