Clinical outcome and prognostic factors in renal medullary carcinoma. A pooled analysis from 18 years of medical literature.
Bibliographic record
Abstract
INTRODUCTION: We describe clinical features and prognostic factors of renal medullary carcinoma (RMC) by performing a pooled analysis of all reported cases since 1995. METHODS: A systematic search was performed to identify all articles describing patients with medullary renal cancer until February 2013. Survivals were estimated using Kaplan-Meier method with 95% confidence intervals and compared across the groups using the log-rank test. The following factors were evaluated using the Cox proportional hazards model: association of extension of disease at diagnosis, response to therapy, and surgical treatment of primary tumour with overall. RESULTS: A total 47 articles were selected; these described 165 patients with RMC plus 1 from our centre. The median age was 21 years and 98% of cases had the sickle cell trait. The mean size of the primary tumours was 6.0 cm, with an involvement of loco-regional lymph nodes in 71% of cases. The overall survival at diagnosis was 4.0 months in metastatic patients and 17.0 months in non-metastatic patients. Patients who received platinum-paclitaxel-gemcitabine had longer control of the disease when compared to topoisomerase inhibitors or targeted therapies. The multivariate analysis confirmed that the advanced stage at diagnosis increased the risk of death of about threefold. CONCLUSION: RMC is a tumour with poorer prognosis; based on these results, platinum-based chemotherapy is the preferred systemic treatment. Even if radical nephrectomy as an up-front strategy did not report a survival benefit, it may be considered to palliate local symptoms and to perform a correct diagnosis.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".