09 / FAST (Frailty And Sarcopenia Trials): Poor agreement between commonly-used frailty assessments in elective colorectal surgical patients.
Bibliographic record
Abstract
Background and Goal of Study:Frailty assessment may be of value to identify high-risk surgical patients, highlighting additional post-operative care needs. A number of frailty assessment tools exist but there is uncertainty as to which is best in surgical patients. This study investigated the agreement between 9 widely accepted frailty assessments.Materials and Methods:Elective colorectal surgical patients were assessed using 9 frailty tools, as part of the FAST study. The clinical frailty scale (CFS); 36-point accumulative deficit (AD); frailty phenotype (FP); frailty, non-disabled tool (FiND); fatigue, resistance, ambulatory, illness and loss of weight tool (FRAIL); Edmonton frailty scale (EFS); Gerontopole frailty screening tool (GFST) polypharmacy as defined by 5+ medications (Poly); and PRISMA 7 frailty score (PRISMA) were used. Cohenu2019s kappa statistic was used to assess agreement between tools. Ethical approval has been granted for the FAST studies by a UK National Health Service Research Ethics Committee.Results and Discussion:95 patients were recruited into FAST, mean age 74 (SD 6.2) years with 7 over85. 66/95 patients had metastatic disease. Frailty was identified by the different measurements (Table 1).There was significant agreement between the different frailty tools ranging from K=0.096 to K=0.590 (figure 1).Conclusion(s):Frailty tools have limited agreement in a surgical population. The adoption of a frailty assessment into a surgical pathway requires scientific and clinical rigour to ensure the optimal assessment is used, providing clinically significant information. We are currently exploring, as part of the FAST study, which assessment has the strongest validity in relation with post-operative adverse outcomes.
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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 | 0.040 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".