Interventions and Outcomes for Neoadjuvant Treatment of T4 Colon Cancer: A Scoping Review
Bibliographic record
Abstract
While adjuvant treatment of colon cancers that penetrate the serosa (T4) have been well-established, neoadjuvant strategies have yet to be formally evaluated. Our objective was to perform a scoping review of eligibility criteria, treatment regimens, and primary outcomes for neoadjuvant approaches to T4 colon cancer. A librarian-led, systematic search of MEDLINE, Embase, Cochrane Library, Web of Science, and CINAHL up to 11 February 2020 was performed. Primary research evaluating neoadjuvant treatment in T4 colon cancer were included. Screening and data abstraction were performed in duplicate; analyses were descriptive or thematic. A total of twenty studies were included, most of which were single-arm, single-center, and retrospective. The primary objectives of the literature to date has been to evaluate treatment feasibility, tumor response, disease-free survival, and overall survival in healthy patients. Conventional XELOX and FOLFOX chemotherapy were the most commonly administered interventions. Rationale for selecting a specific regimen and for treatment eligibility criteria were poorly documented across studies. The current literature on neoadjuvant strategies for T4 colon cancer is overrepresented by single-center, retrospective studies that evaluate treatment feasibility and efficacy in healthy patients. Future studies should prioritize evaluating clear selection criteria and rationale for specific neoadjuvant strategies. Validation of outcomes in multi-center, randomized trials for XELOX and FOLFOX have the most to contribute to the growing evidence for this poorly managed disease.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| 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) | 0.007 | 0.001 |
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".