Neoadjuvant therapy <i>versus</i> direct to surgery for T4 colon cancer: meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite persistently poor oncological outcomes, approaches to the management of T4 colonic cancer remain variable, with the role of neoadjuvant therapy unclear. The aim of this review was to compare oncological outcomes between direct-to-surgery and neoadjuvant therapy approaches to T4 colon cancer. METHODS: A librarian-led systematic search of MEDLINE, Embase, the Cochrane Library, Web of Science, and CINAHL up to 11 February 2020 was performed. Inclusion criteria were primary research articles comparing oncological outcomes between neoadjuvant therapies or direct to surgery for primary T4 colonic cancer. Based on PRISMA guidelines, screening and data abstraction were undertaken in duplicate. Quality assessment was carried out using Cochrane risk-of-bias tools. Random-effects models were used to pool effect estimates. This study compared pathological resection margins, postoperative morbidity, and oncological outcomes of cancer recurrence and overall survival. RESULTS: Four studies with a total of 43 063 patients met the inclusion criteria. Compared with direct to surgery, neoadjuvant therapy was associated with increased rates of margin-negative resection (odds ratio (OR) 2.60, 95 per cent c.i. 1.12 to 6.02; n = 15 487) and 5-year overall survival (pooled hazard ratio 1.42, 1.10 to 1.82, I2 = 0 per cent; n = 15 338). No difference was observed in rates of cancer recurrence (OR 0.42, 0.15 to 1.22; n = 131), 30-day minor (OR 1.12, 0.68 to 1.84; n = 15 488) or major (OR 0.62, 0.27 to 1.44; n = 15 488) morbidity, or rates of treatment-related adverse effects. CONCLUSION: Compared with direct to surgery, neoadjuvant therapy improves margin-negative resection rates and overall survival.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.061 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".