How to Screen for Hereditary Cancers in General Pathology Practice
Bibliographic record
Abstract
CONTEXT: -As a pathologist, an awareness of the particular diagnoses that can serve as "sentinels" for an underlying genetic syndrome can be incredibly beneficial to patients and their families. This is a complex and ever-changing field of medicine, where remaining up to date with diagnostic and treatment options is challenging. Simply raising the possibility of an underlying syndrome may not, in itself, be diagnostic; however, this may present an opportunity for genetic assessment, and possibly early intervention or primary prevention of disease in the kindred. In the last decade, immunohistochemistry has emerged as a useful tool in hereditary cancer screening. This is best exemplified by the use of mismatch repair immunohistochemistry as a screening tool in colorectal and endometrioid carcinomas. Reflex testing of all tumors for deficiencies in these proteins is now resulting in superior identification and treatment of Lynch-associated cancers, and families harboring these syndromes. Despite the success and potential value of immunohistochemistry as a genetic screening tool, hematoxylin-eosin morphology remains a valuable tool for hereditary cancer screening and is the focus of this article. OBJECTIVE: -To highlight the utility of hematoxylin-eosin morphology as a valuable tool for hereditary cancer screening. DATA SOURCES: -Primary literature review with PubMed. CONCLUSIONS: -Recognition of tumors associated with cancer predisposition may identify individuals and families at high risk for cancer and may also have peridiagnostic utility with regard to implications for targeted therapy.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".