Utility of surveillance following curative intent resection of metastases.
Bibliographic record
Abstract
6562 Background: Surveillance is frequently conducted after the completion of curative treatment in early stage cancers to detect resectable recurrences. As more stage IV patients undergo curative resection of metastases (CRM), surveillance of such cases is increasingly performed, but its utility is unclear. Using a cohort of metastatic colorectal cancer (mCRC) patients, we aimed to 1) characterize surveillance patterns in a population-based setting and 2) examine if surveillance contributed to improved outcomes. Methods: Patients diagnosed with mCRC from 1995 to 2010 and referred to any 1 of 5 cancer centers in British Columbia were reviewed. Using Cox regression models that adjusted for confounders, we identified predictors of overall survival (OS) in patients who underwent CRM. Recurrences were categorized into those detected by surveillance vs symptoms and whether further attempts at CRM were feasible. Results: We identified 2082 mCRC patients of whom 254 proceeded to CRM. Median age was 63, 52% were men, 44% had de novo stage IV disease, 56% received perioperative chemotherapy, and 17%/66% had lung/liver metastases, respectively. Surveillance practices after CRM varied widely, but included clinical examination (85%), CEA (86%), imaging (89%) and endoscopy (28%) in the first 5 years. The median OS of CRM cases was 40.9 months, including 191 (75%) recurrences. The median time to recurrence was 10.2 months. Recurrences were detected by surveillance in 152 (80%) cases, and proceeded to a second CRM in 41 (21%). Compared to recurrences detected by symptoms, those based on surveillance were more likely to proceed to another CRM (25% vs. 11%, p < 0.001). Adjusting for confounders, surveillance (HR 0.61 95% CI 0.39-0.94, p = 0.026) and a second CRM (HR 0.53, 95% CI 0.34-0.82, p = 0.004) were independently correlated with improved OS. Conclusions: In this population-based cohort of mCRC patients, the majority recurred after the initial CRM, but recurrences detected by surveillance were more amenable to a subsequent CRM. While surveillance was performed in most cases, significant variations in practice were observed, underscoring the need for wider dissemination of evidence-based guidelines for the surveillance of selected metastatic disease.
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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 | 0.005 |
| 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".