Pre‐operative frailty is predictive of adverse post‐operative outcomes in colorectal cancer patients
Bibliographic record
Abstract
BACKGROUND: An increasing number of elderly patients are presenting for elective surgery. Pre-operative risk assessment in this population is inexact due to the complex interplay between age, comorbidity and functional status. Frailty assessment may provide a surrogate measure of a patient's physiological reserve and aid operative decision-making. The aim of this study is to determine the association between pre-operative frailty, as assessed using the Edmonton Frail Scale, and post-operative outcomes in elderly patients undergoing elective colorectal cancer surgery. METHODS: A prospective analysis of 86 patients over the age of 65 undergoing elective colorectal cancer surgery at a tertiary centre between October 2017 and October 2018 was performed. Frailty assessment was conducted pre-operatively using the Edmonton Frail Scale. Primary outcomes included length of stay and post-operative complication rates. Multivariable logistic regression analyses were used to determine the influence of frailty on post-operative outcomes including mortality, prolonged hospital admission, complication rates and quality of life. RESULTS: Of 86 patients, 12 (14.0%) were identified as frail. Frailty was associated with a significantly increased median length of stay (20 days versus 6 days, incidence rate ratio 2.83, P < 0.01) and a significantly increased risk of major post-operative complications (50.0% versus 6.7%, odds ratio 13.8, P < 0.01). Frailty was not associated with a significant reduction in quality of life scores at 30 and 90 days post-operatively. CONCLUSION: Frailty is associated with adverse post-operative outcomes in elderly patients undergoing elective colorectal cancer surgery. Frailty assessment is an important component of pre-operative risk assessment and may identify targets for pre-operative optimisation.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".