Comparative Genomic Hybridization of Wilms’ tumor
Bibliographic record
Abstract
Cytogenetic analysis of solid tumors including Wilms' tumor is challenging due to poor chromosome morphology, complexity of abnormalities, and to the possibility of stromal cell overgrowth in tissue culture. Molecular cytogenetic techniques such as chromosomal comparative genomic hybridization (CGH) have improved the diagnosis of chromosomal aberrations in Wilms' tumor since they can provide results based on the analysis of DNA from nondividing cells. However, chromosomal CGH provides only a limited resolution across the whole genome, which is not different than routine cytogenetic analysis (gains or losses of less than one chromosome band or 10 Mb are not detectable by routine cytogenetics or chromosomal CGH). More recently, the development of genomic arrays opened the possibility of assessing the whole genome at a much higher resolution at a sub-microscopic or sub-band level. Based on the principle of chromosomal CGH, this approach, frequently termed array-CGH, opens the possibility to find invisible changes at the whole genome level not only in abnormal but also in normal tumor karyotypes. Here, we discuss the main technical features, benefits, and limitations of the above three techniques as applied to Wilms' tumor and summarize the main advances in our knowledge about the genetic changes of Wilms' tumor and their clinical relevance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".