Structured assessment and followup for patients with hereditary kidney tumour syndromes
Bibliographic record
Abstract
INTRODUCTION: Optimal clinical assessment and subsequent followup of patients with or suspected of having a hereditary renal cell carcinoma syndrome (hRCC) is not standardized and practice varies widely. We propose protocols to optimize these processes in patients with hRCC to encourage a more uniform approach to management that can then be evaluated. METHODS: A review of the literature, including existing guidelines, was carried out for the years 1985-2015. Expert consensus was used to define recommendations for initial assessment and followup. RESULTS: Recommendations for newly diagnosed patients' assessment and optimal ages to initiate followup protocols for von Hippel Lindau disease (VHL), hereditary papillary renal cancer (HPRC), hereditary leiomyomatosis with renal cell carcinoma (HLRCC), Birt-Hogg-Dubé syndrome (BHD), familial paraganglioma-pheochromocytoma syndromes (PGL-PCC), and tuberous sclerosis (TSC) are proposed. CONCLUSIONS: Our proposed consensus for structured assessment and followup is intended as a roadmap for the care of patients with hRCC to guide healthcare providers. Although the list of syndromes included is not exhaustive, the document serves as a starting point for future updates.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".