Impact of Patient and Provider Characteristics on the Treatment and Outcomes of Colorectal Cancer
Bibliographic record
Abstract
While the management and prognosis of colorectal cancer are largely dependent on clinical features such as tumor stage, there is considerable variation in treatment and outcome not explained by traditional prognostic factors. To guide efforts by researchers and health-care providers to improve quality of care, we review studies of variation in treatment and outcome by patient and provider characteristics. Surgeon expertise and case volume are associated with improved tumor control, although surgeon and hospital factors are not associated consistently with perioperative mortality or long-term survival. Some studies indicate that patients are less likely to undergo permanent colostomy if they are treated by high-volume surgeons and hospitals. Differences in treatment and outcome of patients managed by health maintenance organizations or fee-for-service providers have not generally been found. Older patients are less likely to receive adjuvant therapy after surgery, even after adjustment for comorbid illness. In the United States, black patients with colorectal cancer receive less aggressive therapy and are more likely to die of this disease than white patients, but cancer-specific survival differences are reduced or eliminated when black patients receive comparable treatment. Patients of low socioeconomic status (SES) have worse survival than those of higher SES, although the reasons for this discrepancy are not well understood. Variations in treatment may arise from inadequate physician knowledge of practice guidelines, treatment decisions based on unmeasured clinical factors, or patient preferences. To improve quality of care for colorectal cancer, a better understanding of mechanisms underlying associations between patient and provider characteristics and outcomes is required.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".