Screening and testing practices for Lynch syndrome in Nova Scotians with endometrial cancer: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Identifying people with Lynch syndrome, a genetic condition predisposing those affected to colorectal, endometrial and other cancers, allows for implementation of risk-reducing strategies for patients and their families. The goal of this study was to describe screening and testing practices for this condition among people with endometrial cancer in Nova Scotia, Canada, and to determine the prevalence of Lynch syndrome in this population. METHODS: All patients diagnosed with endometrial cancer in Nova Scotia between May 1, 2017, and Apr. 30, 2020 were identified through a provincial gynecologic oncology database. Patients from out of province were excluded. We collected age, body mass index, tumour mismatch repair protein immunohistochemistry results, personal and family histories, and germline testing information for all patients. RESULTS: We identified 465 people diagosed with endometrial cancer during the study period. Most were aged 51 years or older, and had obesity and low-grade early-stage endometrioid tumours. Tumour immunohistochemistry testing was performed in 444 cases (95.5%). Based on local criteria, 189 patients were eligible for genetic counselling, of whom 156 (82.5%) were referred to medical genetics. Of the 98 patients who underwent germline testing, 9 (9.2%) were diagnosed with Lynch syndrome. INTERPRETATION: The prevalence of Lynch syndrome was at least 1.9% (9/465) in this population. Our results illustrate successful implementation of universal tumour testing; however, there remains a gap in access to genetic counselling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".