Evaluation of the impact of various definitions of rurality on the prediction of prostate cancer progression
Bibliographic record
Abstract
This study evaluates whether three definitions of rural and urban residence predict prostate cancer progression. People were classified as urban or rural using three definitions: rural and small town (RST), Beale's rural-urban continuum codes, and the Rurality Index of Ontario (RIO) 2008 score. This was a chart-based cohort study of males with prostate cancer who underwent external beam radiation therapy (EBRT) in the Regional Cancer Program at Health Sciences North in Sudbury, Ontario from 1996 to 2003. Data indicative of each of the three definitions were used as predictors in Cox regression analysis for the period of 1,000 to 3,000 days after initial diagnosis and as the basis for dichotomous strata in a log rank test. Complete data were acquired from 629 charts. There was no significant association between any of the three definitions of rurality and prostate cancer progression. However, a Beale-based dichotomization led to survival differences using the log rank test. Beale stratification was potentially sensitive to relevant differences in populations that were not represented by the other two definitions. Given the moderate correlations between the different rurality scores, there may be merit to considering multiple rurality scores as they may lead to different cancer progression outcomes in some situations.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".