Oncological outcomes of local excision versus radical surgery for early rectal cancer in the context of staging and surveillance: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Local resection (LR) methods for rectal cancer are generally considered in the palliative setting or for patients deemed a high anaesthetic risk. This systematic review and meta-analysis aimed to compare oncological outcomes of LR and radical resection (RR) for early rectal cancer in the context of staging and surveillance assessment. METHODS: A literature search of MEDLINE, Embase and Emcare databases was performed for studies that reported data on clinical outcomes for both LR and RR for early rectal cancer from January 1995 to April 2023. Meta-analysis was performed using random-effect models and between-study heterogeneity was assessed. The quality of assessment was assessed using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias 2.0 tool for randomised controlled trials. RESULTS: 39 %) when compared to LR. However, when staging and surveillance methods were clearly adopted in LR cases, there was an improvement in R0 rates (96.7 % vs 85.6 %), 5-year disease-free survival (93.0 % vs 77.9 %) and overall survival (81.6 % vs 79.0 %) compared to when staging and surveillance was not reported/performed. CONCLUSIONS: LR may be appropriate for selected patients without poor prognostic factors in early rectal cancer. This study also highlights that there is currently no single standardised staging or surveillance approach being adopted in the management of early rectal cancer. A more specified and standardised preoperative staging for patient selection as well as clinical and image-based surveillance protocols is needed.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".