The impact of frailty on clinical outcomes in colorectal cancer surgery: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: The majority of colorectal cancer is diagnosed in people aged >65 years, yet the elderly are less likely to undergo curative surgery. Chronological age is poorly correlated with post-operative outcomes and is not an acceptable measure of risk. Conversely, frailty is a strong predictor of poor post-operative outcomes and presents an opportunity for optimisation. This systematic review aims to assess the evidence between frailty and outcomes in patients of all ages undergoing colorectal cancer resections and to compare the predictive value of frailty status to that of age alone. METHODS: The review was registered on Prospero, CRD42019150542. PubMed was searched for articles reporting outcomes for frail patients undergoing elective or emergency colorectal cancer resection up until August 2019. All studies reporting outcomes in frail patients were deemed eligible for inclusion and assessed according to the PRISMA guidelines. RESULTS: Of the 143 identified studies, 17 were eligible for inclusion. Study type, frailty assessments and outcomes measured were highly variable. 'Frailty' was associated with significantly higher rates of post-operative complications (7/7 studies), post-operative mortality (5/7 studies), readmission (3/4 studies) and length of stay (3/3 studies). Seven of 11 studies reported no association between age and adverse outcomes. CONCLUSION: Frailty is a predictor of poor clinical outcomes in patients undergoing surgery for colorectal cancer. Standardisation of frailty assessment and outcome measure is needed. Accurate risk stratification of patients will allow us to make informed treatment decisions, identify patients who may benefit from preoperative intervention and tailor post-operative care.
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".