Patterns of Colorectal Cancer Care in the United States and Canada: A Systematic Review
Bibliographic record
Abstract
Colorectal cancer is the third most common cancer in the United States and Canada. Given the high incidence and increased survival of colorectal cancer patients, prevalence is increasing over time in both countries. Using MEDLINE, we conducted a systematic review of the literature published between 2000 and 2010 to describe patterns of colorectal cancer care. Specifically we examined data sources used to obtain treatment information and compared patterns of cancer-directed initial care, post-diagnostic surveillance care, and end-of-life care among colorectal cancer patients diagnosed in the United States and Canada. Receipt of initial treatment for colorectal cancer was associated with the anatomical position of the tumor and extent of disease at diagnosis, in accordance with consensus-based guidelines. Overall, care trends were similar between the United States and Canada; however, we observed differences with respect to data sources used to measure treatment receipt. Differences were also present between study populations within country, further limiting direct comparisons. Findings from this review will allow researchers, clinicians, and policy makers to evaluate treatment receipt by patient, clinical, or system characteristics and identify emerging trends over time. Furthermore, comparisons between health-care systems in the United States and Canada can identify disparities in care, allow the evaluation of different models of care, and highlight issues regarding the utility of existing data sources to estimate national patterns of care.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".