Intentionally curative treatment of locally recurrent rectal cancer: a systematic review
Bibliographic record
Abstract
BACKGROUND: There is a lack of outcome data beyond local recurrence rates after primary treatment in rectal cancer, despite more information being necessary for clinical decision-making. We sought to determine patient selection, therapeutic modalities and outcomes of locally recurrent rectal cancer treated with curative intent. METHODS: We searched MEDLINE (1990-2010) using the medical subject headings "rectal neoplasms" and "neoplasm recurrence, local." Selection of cohort studies was based on the primary intention of treatment and availability of at least 1 outcome variable. RESULTS: We included 55 cohort studies comprising 3767 patients; 8 studies provided data on the rate of intentionally curative treatment from an unselected consecutive cohort of patients (481 of 1188 patients; 40%). Patients were symptomatic with pain in 50% (796 of 1607) of cases. Overall, 3088 of 3767 patients underwent resection. The R0 resection rate was 56% (1484 of 2637 patients). The rate of external beam radiotherapy was 100% in 9 studies, 0% in 5 studies, and ranged from 12% to 97% in 37 studies. Overall postoperative mortality was 2.2% (57 of 2515 patients). Five-year survival was at least 25%, with an upper limit of 41% in 11 of 18 studies including at least 50 resections. We found a significant increase in reported survival rates over time (r2 = 0.214, p = 0.007). CONCLUSION: More uniformity in treatment protocols and reporting on outcomes for locally recurrent rectal cancer is warranted. The observed improvement of reported survival rates in time is probably related to better patient selection and optimized multimodality treatment in specialized centres.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".