Advances in sarcoma molecular diagnostics
Bibliographic record
Abstract
Sarcomas are cancers of mesenchymal origin with the potential to arise in diverse anatomic locations. With over 80 subtypes, which often demonstrate overlapping morphologies, sarcomas frequently require ancillary testing to enable accurate classification. Pathognomonic driver mutations can often be leveraged for diagnostic purposes and include fusion genes, amplification events, and recurrent point mutations. Until relatively recently, the major clinical molecular diagnostic tests have been karyotyping, fluorescence in situ hybridization, and polymerase chain reaction; however, these techniques have a number of limitations. Recent technological advances have led to the development of more comprehensive assays with higher throughput, thereby replacing the need for a suite of single gene tests. These approaches include next-generation sequencing, fluorescent bar code hybridization, and DNA methylation profiling, among others. Herein, we review the application of recently developed techniques relevant to the diagnosis of sarcomas, and emerging assays with the potential for future development and clinical implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 0.004 |
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".