The impact of palliative resection (PR) of the primary tumor on overall survival (OS) in metastatic colorectal cancer (mCRC).
Bibliographic record
Abstract
509 Background: The role of PR of the primary tumor in mCRC remains unclear. Using population-based data, we explored the impact of PR on OS. Methods: Patients (pts) with mCRC who were referred to 1 of 5 regional cancer centers in British Columbia between 2006 and 2008 were reviewed (n=802). Pts with prior early stage CRC who relapsed with mCRC were excluded (n=285). We conducted survival analysis using Kaplan Meier methods and determined adjusted hazard ratios (HR) for death using Cox proportional hazards models. A secondary propensity score matched analysis was performed to control for baseline differences between pts who underwent PR and those who did not. Results: A total of 517 pts with mCRC were identified: median age was 63 years (range 23-93), 54% were men, 55% had ECOG 0-1, 76% had a colon primary, and 31% had >1 metastatic site. The majority (n=378; 73%) underwent PR of the primary tumor and a significant proportion (n=327; 63%) received palliative chemotherapy (CT). Compared to pts without PR, those with PR were more likely to be men (62 vs 51%, p=0.03), aged <65 years (63 vs 52%, p=0.03), ECOG 0-1 (61 vs 38%, p<0.0001), and receive palliative CT (68 vs 50%, p=0.0004). PR was associated with improved median OS across groups (Table). The benefit of PR on prognosis persisted in multivariate analysis (HR for death 0.56, 95%CI 0.43-0.72, p<0.0001 for entire cohort; HR 0.51, 95%CI 0.37-0.70, p<0.0001 for individuals who were treated with CT; and HR 0.54, 95%CI 0.34-0.84, p=0.007 for those who did not receive CT). In a propensity score matched analysis that considered age, gender, ECOG, and receipt of palliative CT, prognosis continued to be more favorable in the PR group (HR 0.66, 95% CI 0.50-0.86, p=0.0019). Conclusions: In this population-based analysis, PR of the primary tumor in mCRC was associated with a significant OS benefit. [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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".