Survival after Liver Resection for Metastatic Colorectal Carcinoma in a Large Population
Bibliographic record
Abstract
BACKGROUND: Previous reports of liver resection for metastatic colorectal cancer (CRC) are typically from single centers and cannot account for selection or referral bias. We measured longterm survival after liver resection for metastatic CRC in the province of Ontario, Canada (population 12 million). STUDY DESIGN: The Ontario Cancer Registry is an administrative database that links all hospital records, pathology reports, and vital statistics for patients with a diagnosis of cancer. We used the Registry to identify and obtain information on all patients who underwent liver resection for metastatic CRC in calendar years 1996 to 2004. Pathology reports of the original CRC resection and subsequent liver resections were individually reviewed. RESULTS: Eight hundred forty-one resections were performed at 43 centers across Ontario during the 9-year period, including wedge resection (n = 303; 36%); lobectomy (n = 466; 55%); and trisectionectomy (n = 72; 9%). Ninety-one percent and 54% of resections were performed at teaching and high-volume centers (> 80 resections), respectively. Most liver resections were performed more than 120 days after original CRC operation (672 of 841; 80%). Perioperative mortality was 3%. Unadjusted 1-, 3-, and 5-year survival after liver resection was 88%, 59%, and 43%, respectively. Survival was improved when resection was performed for fewer than 2 tumor nodules, at high-volume centers, or in the years 2001 to 2004. CONCLUSIONS: Results in this population-based series are consistent with those of single-hospital series assessing longterm survival after liver resection for metastatic CRC. These findings support continued efforts to aggressively identify and resect CRC liver metastases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".