Application of Canadian hereditary renal cell carcinoma risk criteria to a population database.
Bibliographic record
Abstract
621 Background: Canadian criteria for identifying patients (pts) and families at risk for hereditary renal cell carcinoma (RCC) were published in 2013. They included characteristics for pts with RCC (age ≤ 45 years, bilateral or multifocal tumours, associated medical conditions and non-clear cell histologies with unusual features) and for any pts who have a family history of specific clinical or genetic diagnoses associated with renal neoplasms. The clinical impact of these criteria on genetic testing had yet to be evaluated. Methods: The Canadian hereditary RCC risk criteria were applied to pts from 16 centres in the Canadian Kidney Cancer Information System prospective database. The primary endpoint was the proportion of pts who met at least one criterion. Secondary endpoints included the number of pts with more than one criterion and the number of pts receiving genetic testing (with or without at risk criteria). Results: From January 2011 to May 2017, 8097 pts were entered in the database. 2827 (35%) met at least one criterion for genetic testing. The majority (83%) met just 1 criterion, while 16% met 2 criteria. The criterion of non-clear cell histology with unusual features contributed the largest proportion of at risk pts (59%), followed by age ≤ 45 years (29%), then first or second degree relative with renal tumour (16%). 69 pts underwent genetic testing, with 59 being classified at risk ( < 3% of at risk). Details about the genetic testing results will be presented. Conclusions: The application of the Canadian hereditary RCC risk criteria to a population database resulted in 35% of pts being identified at risk for hereditary RCC. However, the true incidence of hereditary RCC in this population is unknown as most pts did not undergo genetic testing, and thus the sensitivity or specificity of the criteria cannot be determined. The low proportion of at risk pts that underwent genetic testing was disappointing and highlights that there may be gaps in reporting, knowledge and/or barriers in access to genetic testing. The results have helped determine the proportion of at risk pts in Canada, what criteria are most common, and importantly, have established a foundation and benchmark to improve upon.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".