Bibliographic record
Abstract
INTRODUCTION: Leiomyosarcoma of the kidney and renal pelvis is a rare tumor that, on the basis of limited data, has been ascribed a particularly poor prognosis compared to other subtypes of renal malignancy. Here the population-based Surveillance, Epidemiology, and End Results (SEER) registry is used to study the survival of renal leiomyosarcomas. METHODS: There were 95,935 cases of invasive cancer of the kidney and renal pelvis retrieved from the SEER registry to provide 112 cases of leiomyosarcoma. Kaplan-Meier survival estimates and Cox proportional hazard models were constructed to compare the survival of leiomyosarcomas to other renal malignancies. RESULTS: Leiomyosarcomas constituted 0.12% of all invasive renal malignancies. They exhibited a median overall survival of 25 months, with a 25% 5-year overall survival, and a 60% 5-year cause-specific survival. Multivariate analysis of all renal malignancies together revealed that cancer stage was the strongest predictor for overall survival followed by age, histological grade, histological subtype, tumor size, and gender. The hazard ratio for leiomyosarcoma in this analysis was intermediate compared to the other malignancies. When leiomyosarcomas were analyzed separately, the major determinants to overall survival were stage and age at diagnosis. Kaplan-Meier analysis revealed that the overall survival curve for renal leiomyosarcoma essentially superimposed that of transitional cell carcinoma, and was better than that of clear cell carcinoma. These results provide a more optimistic outlook than has been conventionally afforded to this tumor.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".