Determining Generalizability of the Canadian Kidney Cancer information system (CKCis) to the Entire Canadian Kidney Cancer Population
Bibliographic record
Abstract
INTRODUCTION: The Canadian Kidney Cancer information system (CKCis) has prospectively collected data on patients with renal tumors since January 1, 2011 from 16 sites within 14 academic centers in six provinces. Canadian kidney cancer experts have used CKCis data to address several research questions. The goal of this study was to determine if the CKCis cohort is representative of the entire Canadian kidney cancer population, specifically regarding demographic and geographic distributions. METHODS: digit=0) and by Canadian urban boundary files. To determine the proportion of renal cell carcinoma (RCC) patients that CKCis captures, CKCis accruals were compared to projected Canadian Cancer Society RCC incidence in 2016-2017 and the incidence from the 2016 Canadian Cancer Registry. To determine if the CKCis baseline data is representative, it was compared to registry data and other published data when registry data was not available. RESULTS: This CKCis cohort includes 10 298 eligible patients: 66.6% male, median age 62.6 years; 14.6% had metastatic disease at the time of diagnosis and 70.4% had clear-cell carcinomas. The CKCis cohort captures about 1250 patients per year, which represents approximately 20% of the total kidney cancer incidence. The proportion of patients captured per province did vary from 13-43%. Rural patients make up 17% of patients, with some baseline differences between rural and urban patients. There appears to be no major differences between CKCis patient demographics and disease characteristics compared to national data sources. Canadian heat maps detailing patient location are presented. CONCLUSIONS: CKCis contains prospective data on >10 000 Canadian kidney cancer patients, making it a valuable resource for kidney cancer research. The baseline demographic and geographic data do appear to include a broad cross-section of patients and seem to be highly representative of the Canadian kidney cancer population. Moving forward, future projects will include determining if CKCis cancer outcomes are also representative of the entire Canadian kidney cancer population and studying variations across provinces and within rural vs. urban areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".