Prognostic Value of Quantifying Apoptosis Factor Expression in Favorable Histology Wilms Tumors
Bibliographic record
Abstract
Important in the homeostasis of normal tissues, apoptosis can be altered to favor cell survival within tumors. High expression of survivin, an inhibitor of apoptosis, and absence of caspase 8, a pro-apoptotic enzyme, independently correlate with poor outcomes in several tumor types. Favorable histology Wilms tumor has a remarkably high cure rate; as a result, the focus of therapy is now aimed at reducing treatment-related morbidity. With the goal of safely reducing therapy in select subgroups of patients, the authors investigated whether the levels of apoptotic factors in tumors could predict the risk for recurrence. Tumor apoptotic factor levels were surveyed in a case-control study from the National Wilms Tumor Study 5 (NWTS-5) and measured via quantitative real-time RT-PCR. Survivin and caspase 8 levels were surveyed in 92 primary tumor specimens and SMAC, Bid, and CD95 were surveyed in 24 specimens. All four pro-apoptotic factors studied (caspase 8, SMAC, Bid, and CD95) were analyzed individually and in relation to survivin expression. Although survivin mRNA was present at markedly greater levels than in normal kidney, none of the factors assayed independently or as a ratio was associated with stage of disease or risk for tumor recurrence in this group of tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".