Tumors of the Digestive System: Comprehensive Review of Ancillary Testing and Biomarkers in the Era of Precision Medicine
Bibliographic record
Abstract
Immunotherapy has remained at the vanguard of promising cancer therapeutic regimens due to its exceptionally high specificity for tumor cells and potential for significantly improved treatment-associated quality of life compared to other therapeutic approaches such as surgery and chemoradiation. This is especially true in the digestive system, where high rates of mutation give rise to a host of targetable tumor-specific antigens. Many patients, however, do not exhibit measurable improvements under immunotherapy due to intrinsic or acquired resistance, making predictive biomarkers necessary to determine which patients will benefit from this line of treatment. Many of these biomarkers are assessed empirically by pathologists according to nuanced scoring criteria and algorithms. This review serves to inform clinicians and pathologists of extant and promising upcoming biomarkers predictive of immunotherapeutic efficacy among digestive system malignancies and the ancillary testing required for interpretation by pathologists according to tumor site of origin.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.002 | 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".