An alternative approach to identify women at risk for colorectal cancer.
Bibliographic record
Abstract
1513 Background: Hereditary colorectal cancer (CRC) is preventable; however, identification of individuals at sufficiently high risk to warrant heightened surveillance is difficult. Lynch Syndrome (LS) is an inherited cancer syndrome due to germline mutation in a DNA mismatch repair gene. For women with LS, the lifetime risk of endometrial cancer (EC) is 64% and CRC is 54%. Fifty percent of women with LS will present with EC or ovarian cancer prior to CRC. Therefore, women with LS associated EC represent an ideal group for CRC prevention. The optimal method to identify women with LS associated EC is not known. The purpose of this study was to determine the utility of Amsterdam II and Society of Gynecologic Oncology (SGO) Criteria (modified Bethesda criteria that use EC as the sentinel cancer) in identifying women with LS associated EC. Our ultimate goal is to identify women at increased risk of CRC. Methods: Immunohistochemistry (IHC) for DNA mismatch repair proteins and MLH1 methylation analyses were used to identify LS associated EC among 388 women. EC was designated as LS if there was loss of mismatch repair protein expression. Absence of MLH1 methylation was required to confirm LS in tumors with MLH1 protein loss. Results: Fifty-nine (15.2%) of the EC patients tested had LS. These patients are summarized in the table. Conclusions: Clinical criteria to detect LS identify 17/59 (29%) - 44/59 (74%) of women who present with EC first. EC with MSH2 loss is most likely to occur in younger women and women with positive family history of EC and CRC, features classically associated with LS. In general, the MSH6 mutation is associated with older age at diagnosis and fewer familial CRCs, however, we found a large number of MLH1 (50%) and PMS2 (86%) cases diagnosed at greater than 50 years with no family history of CRC. Our data suggest that classic clinical screening criteria are inadequate to detect patients with LS who present with EC, potentially missing up to 25% of these patients. [Table: see text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".