What’s new in the WHO 2022 classification of kidney tumours?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
tumours (5th edition) has significantly improved our understanding of the morphologic, immunohistochemical, and molecular characteristics of renal tumours. The aim of this review is to outline the most important changes and diagnostic updates in the WHO 2022 classification of kidney tumours. A major change in this edition is the grouping of renal tumours into broader categories that include “clear cell renal tumours”, “papillary renal tumours”, “oncocytic and chromophobe renal tumours”, “collecting duct tumours” as well as adding two categories of “other renal tumours” and “molecularly defined renal carcinomas”. Novel entities included in the WHO 2022 classification are eosinophilic solid and cystic renal cell carcinoma (ESC RCC), anaplastic lymphoma kinase (ALK)-rearranged RCC and ELOC (formerly TCEB1)-mutated RCC. The category of “other renal tumours” includes a group of diverse, unrelated renal tumours that do not fit into other categories. The group of “molecularly defined renal carcinomas” reflects recent discoveries in the renal tumour genomics. These molecularly-defined renal entities demonstrate a set of morphologic features reflecting genotype-phenotype relationships. Final diagnosis of such entities rests on phenotypic and immunohistochemical (IHC) correlation, usually associated with IHC surrogate makers that reflect specific genetic abnormalities.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it