Translating Gene Expression Into Clinical Care: Sarcomas As a Paradigm
Bibliographic record
Abstract
Whereas most solid tumors are characterized by considerable genetic instability and molecular heterogeneity, sarcomas include many subtypes with very specific underlying molecular events driving oncogenesis. Gene expression profiling and other modern techniques have consequently had particular success in identifying the critical biologic pathways active in specific sarcomas, yielding insights which can be translated into useful diagnostic biomarkers. Public availability of data sets and new sequencing-based technologies will accelerate this process. Molecular studies have also identified oncogenic pathways of particular importance in sarcomas which can be targeted by investigational drugs. Examples include histone deacetylases in translocation-associated sarcomas of young adults, Akt/mammalian target of rapamycin in pleomorphic sarcomas, and macrophage colony-stimulating factor in tenosynovial giant cell tumor. Despite challenges in organization and accrual, future clinical trials of sarcomas need to be designed that take into account specific molecular subtypes as distinct diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".