Clinical predictors of a positive test result in patients undergoing genetic evaluation for a hereditary kidney cancer syndrome
Bibliographic record
Abstract
INTRODUCTION: Guidelines are available to assist providers in identifying patients with renal cell carcinoma (RCC) that may benefit from genetic counselling, however, the evidence for these recommendations lacks support from the literature and controversy remains as to who should be referred. We aimed to delineate risk factors associated with a positive genetic test in a real-life cohort of patients with RCC referred to a regional medical genetics unit for evaluation of a hereditary kidney cancer syndrome. METHODS: Patients with a diagnosis of RCC referred to Maritime Medical Genetics Service (Nova Scotia, Canada) from 2006-2017 were reviewed using retrospective data. The primary outcome was identification of clinical features that were associated with a positive test result. Logistic regression models were used for analysis. RESULTS: A total of 135 patients were referred to medical genetics for evaluation; 102 patients were evaluated, 75 underwent testing, and 74 were included in the final analysis. Five patients tested positive: three Birt-Hogg-Dubé, one Cowden syndrome, and one Von Hippel-Lindau. Presence of dermatological lesions (specifically fibrofolliculomas) and more than two high-risk features were the only predictors of a positive test result. CONCLUSIONS: The presence of dermatological lesions and more than two high-risk features are the only predictors of a positive test result in patients with a suspected hereditary kidney cancer syndrome. These findings are not reflected in current guidelines, and the clinical implementation of our results may improve the identification of high-risk patients for genetic counselling.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".