Is there a minimum percentage of sarcomatoid component required to affect outcomes of localised renal cell carcinoma?
Bibliographic record
Abstract
OBJECTIVE: To evaluate and compare the outcomes of patients with localised renal cell carcinoma (RCC) with and without sarcomatoid features and the impact of this on cancer recurrence and survival. MATERIAL AND METHODS: The Canadian Kidney Cancer information system database was used to identify patients diagnosed with localised RCC between January 2011 and December 2022. Patients with pT1-T3, n Nx-N0N1, M0 stage and documented sarcomatoid status were included. Patients with sarcomatoid RCC were categorised according to the sarcomatoid component percentage (%Sarc). Inverse probability of treatment weighting scores were used to balance the groups. Cox proportional hazards models were used to assess the impact of sarcomatoid status and %Sarc on recurrence-free and overall survival. RESULTS: A total of 6660 patients (201 with and 6459 without sarcomatoid features) with non-metastatic RCC were included. %Sarc data were available in 155 patients, and the median value was 10%. The weighted analysis revealed that the presence of sarcomatoid features was associated with an increased risk of developing metastasis and increased risk of mortality compared to absence of sarcomatoid features. A %Sarc value >10 was associated with an increased risk of developing metastasis and of mortality compared to a %Sarc value ≤10. CONCLUSIONS: Patients with a %Sarc >10 have an increased risk of recurrence and mortality. These patients may benefit from a more stringent follow-up and %Sarc could represent an important criterion in the risk assessment for adjuvant therapy.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".