Bibliographic record
Abstract
Clinical and cancer cytogenetics is a rapidly evolving discipline. The past decade has seen a dramatic change in molecular biology and fluorescence microscopy. The use of fluorescence in situ hybridization (FISH) technologies has enabled the rapid analysis of cytogenetic specimens as an adjunct to classical cytogenetic analysis. Spectral karyotyping (SKY) is a 24-color, multi-chromosomal painting assay that allows the visualization of all human chromosomes in one experiment. The ability for SKY analysis to detect equivocal or complex chromosomal rearrangements, as well as to identify the chromosomal origins of marker chromosomes and other extra-chromosomal structures, makes this a highly sensitive and valuable tool for identifying recurrent chromosomal aberrations. The SKY has been applied to various tumor groups including hematological malignancies, sarcomas, carcinomas and brain tumors, with the intent of identifying specific chromosomal abnormalities that may provide insight to the genes involved in the disease process as well as identifying recurrent cytogenetic markers for clinical diagnosis and prognostic assessment. The SKY has also been applied for the mouse genome, enabling investigators to extrapolate information from mouse models of cancer to their human counterparts. This review will address the advances that SKY has facilitated in the field of cancer cytogenetics, as well as its variety of application in the cancer research laboratories.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".