Refusal of colorectal cancer surgery in the United States: Predictors and associated cancer-specific mortality in a Surveillance, Epidemiology, and End Results (SEER) cohort
Bibliographic record
Abstract
INTRODUCTION: This study aims to understand patient factors associated with refusal of surgery for nonmetastatic colorectal cancer and the associated cancer-specific mortality. METHODS: Patients diagnosed with nonmetastatic colorectal cancer between 2004 and 2015 from the Surveillance, Epidemiology, and End Results Program were included. RESULTS: A total of 152,731 (99.4%) patients underwent surgery, and 983 (0.6%) refused surgery. Independent predictors of refusal included male sex, older age, minority race, single relationship status, being uninsured, more recent date of diagnosis, having an earlier stage of diagnosis, and rectal versus colon cancer. Refusing surgery for nonmetastatic colorectal cancer increased cancer-specific mortality (adjusted hazard ratio 5.10, 95% confidence interval 4.62-5.62). CONCLUSION: Most patients diagnosed with nonmetastatic colorectal cancer undergo surgery in the United States. However, refusal of surgery is increasing and associated with higher cancer-specific mortality. A better understanding of surgical decision making in colorectal cancer is urgently needed.
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 | 0.002 |
| 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 | 0.000 | 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".