Attitudes of Canadian Colorectal Cancer Care Providers towards Liver Transplantation for Colorectal Liver Metastases: A National Survey
Bibliographic record
Abstract
Up to 50% of colorectal cancer (CRC) patients develop colorectal liver metastases (CRLM). The aim of this study was to gauge the awareness and perception of liver transplantation (LT) for non-resectable CRLM, and to describe the current referral patterns and management strategies for CRLM in Canada. Surgeons who provide care for patients with CRC were invited to an online survey through the Canadian Association of General Surgeons, the Canadian Society of Colon and Rectal Surgeons, and the Canadian Society of Surgical Oncology. Thirty-seven surveys were included. The most utilized management strategy for CRLM was to refer to a hepatobiliary surgeon for assessment of metastectomy (78%), and/or refer to medical oncologists for consideration of chemotherapy (73%). Among the respondents, 84% reported that their level of knowledge about LT for CRLM was low, yet the perception of exploring the option of LT for non-resectable CRLM seemed generally favorable (81%). The decision to refer for consideration of LT for CRLM treatment seemed to depend on patient-specific factors and the local hepatobiliary surgeon's recommendation. Providing CRC care providers with educational materials on up-to-date CRLM management may help raise the awareness of the use of LT for non-resectable CRLM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".