Pathologists at the Leading Edge of Optimizing the Tumor Tissue Journey for Diagnostic Accuracy and Molecular Testing
Bibliographic record
Abstract
OBJECTIVES: Tumor biomarker analyses accompanying immuno-oncology therapies are coupled with a tumor tissue journey aiming to guide tissue procurement and allow for accurate diagnosis and delivery of test results. The engagement of pathologists in the tumor tissue journey is essential because they are able to link the preanalytic requirements of this process with pathologic evaluation and clinical information, ultimately influencing treatment decisions for patients with cancer. The aim of this review is to provide suggestions on how cancer diagnosis and the delivery of molecular test results may be optimized, based on the needs and available resources of institutions, by placing the tumor tissue journey under the leadership of pathologists. METHODS: Literature searches on PubMed and personal experience provided the necessary material to satisfy the objectives of this review. RESULTS: Pathologists are usually involved across many steps of the tumor tissue journey and have the requisite knowledge to ensure its efficiency. CONCLUSIONS: The expansion of oncology diagnostic testing emphasizes the need for pathologists to acquire a leadership role in the multidisciplinary effort to optimize the accuracy, completeness, and delivery of diagnoses guiding personalized treatments.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".