A national survey on the management of liver metastases from colorectal cancer (CRC): Comparison of perspectives and expectations among medical oncologists and hepatobiliary surgeons across Canada.
Bibliographic record
Abstract
690 Background: To compare the perspectives of medical oncologists versus hepatobiliary surgeons in the management of liver metastases from CRC. Methods: Medical oncologists and hepatobiliary surgeons across Canada were surveyed to evaluate for criteria used to determine resectability of liver metastases from CRC and availability of multidisciplinary clinics. Results: Of 220 experts surveyed, 145 (66%) responded. 137 (95%) had received specialized training in oncology. Only 37 (26%) respondents reported access to both multi-disciplinary tumor boards and clinics. 109 (75%) reported lack of institutional criteria. Oncologists and surgeons disagreed with regard to absolute contraindications for resection of hepatic metastases. More oncologists reported a higher percentage of liver parenchyma involvement (70% vs. 45%), proximity to major vessels and poor performance status as contraindications compared to surgeons (p < 0.05). All physicians deemed CT as the preferred first-line imaging study for evaluating CRC liver metastases with significantly more surgeons indicating the need for MRI of the liver (p < 0.05). Oncologists and surgeons also showed high discordances with respect to both clinical and radiographic findings in terms of their influence on surgical decision regarding resection of liver metastases (Table). Conclusions: Medical Oncologists and surgeons have discordant criteria with respect to resection of hepatic metastases and the roles they play in the management of these complex patients. Uncertainties and discordances can lead to deficiencies in care, supporting the need for a standardized clinical preoperative risk scoring system for resection of liver metastases from CRC. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".