Generational Expression of Muir-Torre Syndrome in a Canadian Family
Bibliographic record
Abstract
Muir-Torre syndrome (MTS) is a rare autosomal dominant inherited genodermatosis that is considered to be a phenotypic subtype of hereditary nonpolyposis colorectal cancer (HNPCC), commonly referred to as Lynch syndrome. We describe the clinical course of a 57-year-old female patient with MTS. She has a confirmedHMSH2mutation. Recently she presented with two nodular lesions. Histologic examination confirmed these lesions to be sebaceous neoplasms. The patient has a history of endometrial and colorectal adenocarcinoma as well as several nonspecific sebaceous lesions throughout her life. She has a confirmed extensive family history of MTS with both male and female family members harbouring eitherHMLH1orHSMH2mutations. Affected relatives have presented at different ages throughout their lives with cutaneous neoplasms and visceral malignancies, including malignancies rarely associated with MTS. MTS presents a diagnostic challenge for clinicians. The case demonstrates that the management of MTS, a potentially underreported syndrome, requires a multiprofessional approach incorporating vigilance, screening, and expert knowledge for successful diagnosis and potentially improved prognosis for patients and their families. The case also demonstrates the varied heritability of MTS and prompts the question of how MTS is expressed in succeeding generations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".