The location of surgical care for rural patients with rectal cancer: patterns of treatment and patient perspectives
Bibliographic record
Abstract
BACKGROUND: Where cancer patients receive surgical care has implications on policy and planning and on patients' satisfaction and outcomes. We conducted a population- based analysis of where rectal cancer patients undergo surgery and a qualitative analysis of rectal cancer patients' perspectives on location of surgical care. METHODS: We reviewed Manitoba Cancer Registry data on patients with colorectal cancer (CRC) diagnosed between 2004 and 2006. We interviewed rural patients with rectal cancer regarding their preferences and the factors they considered when deciding on treatment location. Interview data were analyzed using a grounded theory approach. RESULTS: From 2004 to 2006, 2086 patients received diagnoses of CRC in Manitoba (colon: 1578, rectal: 508). Among rural patients (n = 907), those with rectal cancer were more likely to undergo surgery at an urban centre than those with colon cancer (46.5% v. 28.8%, p < 0.001). Twenty rural patients with rectal cancer participated in interviews. We identified 3 major themes from the interview data: the decision-maker, treatment factors and personal factors. Participants described varying input into referral decisions, and often they did not perceive a choice regarding treatment location. Treatment factors, including surgeon factors and hospital factors, were important when considering treatment location. Personal factors, including travel, support, accommodation, finances and employment, also affected participants' treatment experiences. CONCLUSION: A substantial proportion of rural patients with rectal cancer undergo surgery at urban centres. The reasons are complex and only partly related to patient choice. Further studies are required to better understand cancer system access in geographically dispersed populations and to support cancer patients through the decision-making and treatment processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".