Impact of travel distance on access to treatment and survival in patients with metastatic colorectal cancer prescribed bevacizumab plus chemotherapy.
Bibliographic record
Abstract
INTRODUCTION: Given Saskatchewan's size and low population density outside of city centres, many rural and remote residents have issues accessing regional oncology services. We performed a study to determine whether travel distance to cancer treatment centres affects first-line treatment accessibility and survival in patients with metastatic colorectal adenocarcinoma. METHODS: Retrospective chart review of patients with stage IV metastatic colorectal adenocarcinoma collected by the Saskatchewan Cancer Agency registry between June 1, 2009, and June 30, 2013. Patients were categorized as living within 100 km of or more than 100 km from the nearest cancer treatment centre offering bevacizumab plus first-line chemotherapy. Main outcome measures were differences in first-line treatment accessibility and overall survival estimates (calculated via the Kaplan-Meier method) between cohorts. RESULTS: = 0.2). CONCLUSION: Neither access to bevacizumab treatment nor survival times for metastatic colorectal adenocarcinoma were significantly different between the cohorts. This suggests that health care providers in Saskatchewan may be doing well in arranging timely access to advanced oncology centres. Future studies with a larger sample, different tumour types or changes to the definition of remoteness are indicated.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".