New molecular assays for cancer diagnosis and targeted therapy.
Bibliographic record
Abstract
Microarray multiplex protein measurement of biomarker-based molecular diagnostic and prognostic cancer testing assays is destined to become a large growth segment of the immunodiagnostic industry. Assays encompass immunohistochemistry, fluorescence in situ hybridization, ELISA and sequencing methods that must comply with stringent regulatory specifications. Current test services available range from single-site service-based assays to multi-laboratory testing. Some tests have regulatory approval, whereas others are regulated by the Clinical Laboratory Improvement Act or the College of American Pathologists, or both. Expectations of personalized medicine are building, and the future is expected to bring truly targeted treatments based on test results. In this review, biomarkers, screening, diagnosis, potential prognosis and treatment pertaining to colorectal, breast, and lung cancers are discussed and evaluated. Assay data are expected to improve clinical indices and treatment algorithms, leading to dynamic disease models for real-time, data-modulated patient management.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Review of molecular assays for cancer diagnosis and targeted therapy; the object is diagnostic technology and its clinical regulation.
The review concerns cancer diagnostic assays and treatments, not research methodology.
Industry-oriented review of molecular diagnostic cancer assays and personalized medicine testing.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".