Prognostic variables in adult Wilms tumour.
Bibliographic record
Abstract
OBJECTIVE: To identify outcomes and prognostic variables that predict survival outcomes in adult Wilms tumour patients. METHODS: We collected data on 128 patients with adult Wilms tumour treated between 1973 and 2006. Six cases from our 2 Canadian centres have not been previously reported. We collected data on the remaining 122 patients from published case reports or case series. Analyzed factors included age, sex, favourable or unfavourable histopathology, clinical stage (I, II, III or IV) and chemotherapy and radiotherapy received. The outcomes studied included overall survival (OS) and disease-specific survival (DSS). Univariate analysis with Kaplan-Meier actuarial methodology and multivariate analyses with Cox regression were used to determine outcomes and predictive clinical factors. RESULTS: The patients' mean age was 26 (range 15-73) years. After a mean follow-up of 54 (range 2-240) months, the OS and DSS of the entire cohort were both 68%. Favourable histopathology predicted superior OS and DSS (both p < 0.001). Higher clinical stage predicted inferior OS and DSS (both p < 0.001). CONCLUSION: Adult Wilms tumour has a poorer prognosis than pediatric Wilms tumour. In adults with Wilms tumour, more aggressive patient-and tumour-specific surveillance and adjunctive therapies than those advocated by pediatric National Wilms Tumor Study guidelines may be warranted, especially in patients with an unfavourable histopathology and higher clinical stage.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".