Gynecologic
Bibliographic record
Abstract
to selected 48 cases of renal carcinomas of various subtypes, 7 oncocytomas and 13 normal kidney.Histological examination focused on the presence of small branching glands, stromal cellularity, ovarian-type stroma, prominent vessels and estrogen and progesterone receptors.Results: This study included 26 CN and 13 MEST.Patient' age (53.2 vs 53.8 years) and tumor size (6.5 vs 6.8 cm) were similar between CN and MEST.CN predominantly affected women (male/female=2/24), while MEST exclusively affected female.CN and MEST had many similar histological features, including size of cysts, stromal cellularity, presence of ovarian-type stroma, calcification and hemorrhage.ER and PR were positive in 4/5 (80%) and 3/5 (60%) CN, and 5/8 (62.5%) and 5/7 (71.4%)MEST.However, prominent vessels (4/13, 30.8%) and small branching glands (53.8%) were exclusively seen in MEST (p=0.000).Clustering analysis demonstrated that CN and MEST had very similar molecular profiles (Figure 1) that were distinct from other renal tumors.Of differentially expressed genes that best distinguish CN/MEST from other subtypes of kidney tumors, the highest and lowest differentially expressed genes are insulin-like growth factor 2 and carbonic anhydrase II, respectively.Conclusions: CN and MEST are related lesions as they share similar clinical, histological and molecular characteristics.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".