The introduction of bevacizumab for the treatment of metastatic colorectal cancer (CRC) in Ontario and its effect on hepatic metastectomy.
Bibliographic record
Abstract
303 Background: Where possible, the optimal treatment for CRC liver metastases is metastectomy. Perioperative chemotherapy may be associated with improved outcomes in this setting. The government of Ontario approved public funding of bevacizumab (Bev) for metastatic CRC in July 2008. For first-line treatment, funding of Bev has been limited to use in combination with FOLFIRI, an irinotecan-containing regimen. However, the best evidence in support of perioperative chemotherapy for CRC liver metastases involves the use of oxaliplatin, not irinotecan. We sought to analyze changes in prescribing patterns, histologic findings, and clinical outcomes since the funding approval for Bev in Ontario. Methods: A single centre, retrospective review was performed of all patients receiving metastectomy for CRLM between 2004 and 2010, and archival hematoxylin-eosin slides were reviewed. Results: A total of 121 patients underwent metastectomy for CRLM during the study period. Of these, 54 were treated with perioperative chemotherapy, 37 before and 16 after funding approval of Bev. A marked increase was seen in Bev and irinotecan-containing regimens since funding approval (p < 0.01). Pathologic examination of the surgical specimens did not reveal any increase in steatosis or steatohepatitis. However, there was a trend towards more R0 resections (p = 0.3) in the post-bev era. A significant reduction in the occurrence of post-operative hepatic dysfunction was observed in the post-Bev era (p = 0.04). Overall survival (OS) and disease-free survival (DFS) were similar. Conclusions: Public funding of Bev-FOLFIRI for metastatic CRC has led to an increased and yet incomplete utilization of this regimen for CRLM. Pathologic and clinical data do not suggest harm and may show early signs of benefit. Further research is needed to determine the optimal utilization of bev in the setting of CRLM. [Table: see text]
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".