Bibliographic record
Abstract
Microarrays began to be used to study gene expression profiles in the mid-1990s, but it was only after 2000 that serious attempts have been made to apply this technology to investigate sarcomas. Microarray technologies provide a comprehensive survey of active molecular pathways and potential molecular targets for diagnosis and treatment, but are challenging to use because of issues of specimen collection, cost, and complexities in experimental design and data analysis. As a discovery-based technique, microarray analyses are most valuable when framed around specific gaps in our knowledge of tumor etiology and progression, challenges in differential diagnosis, and pressing therapeutic needs. To date, microarray analyses of sarcomas support their division into molecularly defined and molecularly heterogeneous categories, and have provided useful diagnostic markers for entities such as gastrointestinal stromal tumors, synovial sarcoma, and dermatofibrosarcoma protuberans. Signatures predicting outcome and response to therapy have been published for Ewing sarcoma and osteosarcoma, and receptor tyrosine kinase expression patterns have suggested novel therapeutic approaches which may be applied to several types of sarcoma. Nevertheless, results need to be interpreted in the context of histopathology and validated by complementary technologies and/or other research groups.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".