Choose and stay on one out of two paths: distinction between clinical versus research genetic testing to identify cancer predisposition syndromes among patients with cancer
Bibliographic record
Abstract
In children with cancer, specific clinical features such as physical anomalies, occurrence of cancer in young relatives, specific cancer histologies, and unique mutation/methylation signatures may indicate the presence of an underlying cancer predisposition syndrome (CPS). The proportion of children with a cancer type suggesting a CPS among all children with cancer is unknown. To determine the proportion of children with cancer types suggesting an underlying CPS among children with cancer. We evaluated the number of children with cancer types strongly associated with CPS diagnosed in Germany between 2007 and 2016. Data were obtained from various sources including two national pediatric pathology reference laboratories for brain and solid tumors, respectively, various childhood cancer trial offices as well as the German Childhood Cancer Registry. Among 21,127 children diagnosed with cancer between 2007 and 2016, 2554 (12.1%) had a cancer type strongly associated with a CPS. The most common diagnoses were myelodysplastic syndrome and juvenile myelomonocytic leukemia, retinoblastoma, malignant peripheral nerve sheath tumor, infantile myofibromatosis, medulloblastoma
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".