A meta-analysis comparing the risk of metastases in patients with rectal cancer and MRI-detected extramural vascular invasion (mrEMVI) vs mrEMVI-negative cases
Bibliographic record
Abstract
BACKGROUND: Pathological extramural vascular invasion (EMVI) is an independent prognostic factor in rectal cancer, but can also be identified on MRI-detected extramural vascular invasion (mrEMVI). We perform a meta-analysis to determine the risk of metastatic disease at presentation and after surgery in mrEMVI-positive patients compared with negative tumours. METHODS: Electronic databases were searched from January 1980 to March 2016. Conventional meta-analytical techniques were used to provide a summative outcome. Quality assessment of the studies was performed. RESULTS: Six articles reported on mrEMVI in 1262 patients. There were 403 patients in the mrEMVI-positive group and 859 patients in the mrEMVI-negative group. The combined prevalence of mrEMVI-positive tumours was 0.346(range=0.198-0.574). Patients with mrEMVI-positive tumours presented more frequently with metastases compared to mrEMVI-negative tumours (fixed effects model: odds ratio (OR)=5.68, 95% confidence interval (CI) (3.75, 8.61), z=8.21, df=2, P<0.001). Patients who were mrEMVI-positive developed metastases more frequently during follow-up (random effects model: OR=3.91, 95% CI (2.61, 5.86), z=6.63, df=5, P<0.001). CONCLUSIONS: MRI-detected extramural vascular invasion is prevalent in one-third of patients with rectal cancer. MRI-detected extramural vascular invasion is a poor prognostic factor as evidenced by the five-fold increased rate of synchronous metastases, and almost four-fold ongoing risk of developing metastases in follow-up after surgery.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.059 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".