Array CGH analysis of pediatric medulloblastomas
Bibliographic record
Abstract
Brain tumors are the second most common childhood cancer. We used high-resolution array comparative genomic hybridization (aCGH) to analyze losses and gains of genetic material from 24 medulloblastomas. The bacterial artificial chromosome clones were ordered on the array, allowing for an average resolution of approximately 420 kilobases. The advantage of this high resolution is that the breakpoints associated with subregional chromosome copy number aberrations can be accurately defined, which in turn allows candidate genes within these regions to be readily defined. In this analysis, we confirmed the frequent involvement of loss of 17p and gain of 17q, although we have now established the position of the breakpoint that consistently lies in the chr17:18318880-19046234 region of the chromosome. Other frequent losses were seen on 8p, 10q, 16q, and 20p, and frequent gains were seen on 2p, 4p, 7, and 19. In addition, the fine-resolution mapping provided by aCGH made it possible to define small chromosome deletions in 1q23.3-q24.2, 2q13.12-q13.2, 6q25-qter, 8p23.1, 10q25.1, and 12q13.12-q13.2. Overall, amplification events were rare, the most common involving MYC (16%), on 8q, although isolated events were seen in 10p11 and 3q.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".