Organ Preserving or Radical Surgery? A Systematic Review and Meta‐Analysis of Transanal Local Excision Versus Total Mesorectal Excision After Neoadjuvant Therapy for Rectal Cancer
Bibliographic record
Abstract
BACKGROUND: Total mesorectal excision (TME) is the standard surgical treatment for rectal cancer. Transanal local excision (TLE) after neoadjuvant chemoradiotherapy (nCRT) is an organ-preserving option, avoiding morbidity of TME. This study compared TLE versus TME following nCRT. METHODS: We searched Medline, PubMed, Embase, Web of Science, Scopus, Cochrane databases, Google Scholar, and CINHAL to 30 April 2025. Eligible studies were adults with nonmetastatic mid or low rectal cancer treated with nCRT followed by TLE or TME. Outcomes included local recurrence, disease free survival (DFS), overall survival (OS), and postoperative complications. Risk of bias was assessed using Cochrane RoB 2 for randomized trials and Newcastle-Ottawa Scale (NOS) for cohorts. RESULTS: Nineteen studies were included. TLE was associated with higher local recurrence in cohorts (RR = 1.823; 95% CI = 1.222-2.720; p = 0.003), but no difference in RCTs (RR = 1.248; 95% CI = 0.618-2.518; p = 0.537). DFS (HR = 1.121; p = 0.174) and OS (HR = 1.032; p = 0.830) did not differ. Postoperative morbidity was lower after TLE (RR = 0.429; p = 0.005). CONCLUSION: Strengths include robust search, study quality and number of patients, while heterogeneity in nCRT protocol, follow up, and complication reporting are limitations. Higher recurrence in TLE in cohorts but not in RCTs suggests safety of TLE when strict selection criteria are applied. REGISTRATION: PROSPERO CRD420251076513.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".