Fine needle aspiration of renal cortical lesions in adults
Bibliographic record
Abstract
The role of fine needle aspiration (FNA) biopsy of renal cortical lesions was controversial in the past because the result of the FNA did not affect clinical management. All renal cortical lesions, except metastasis, were subject to surgical resection. However, with the advances in neoadjuvant targeted therapies, knowledge of the renal cortical tumor histological subtype is critical for tailoring clinical trials and follow-up strategies. At present, there are clinical trials involving the use of novel kinase inhibitors for conventional (clear cell) and papillary renal cell carcinoma. We studied 143 consecutive cases of renal cortical lesions, evaluated after radical or partial nephrectomies over a 2-year period. An air-dried smear and a Thinprep® slide were prepared in all cases. The slides were Diff-Quick and Papanicolaou stained, respectively. The cytology specimens were reviewed and the results were then compared with the histologic diagnosis. Cytology was highly accurate to diagnose conventional RCC, while the accuracy for papillary RCC, chromophobe RCC, and papillary urothelial carcinoma was much lower. Our results indicate that ancillary studies might have an important role in the subclassification of renal cortical neoplasms for targeted treatment.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".