Comparison of the clinical prediction model PREMM <sub>1,2,6</sub> and molecular testing for the systematic identification of Lynch syndrome in colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Lynch syndrome is caused by germline mismatch repair (MMR) gene mutations. The PREMM(1,2,6) model predicts the likelihood of a MMR gene mutation based on personal and family cancer history. OBJECTIVE: To compare strategies using PREMM(1,2,6) and tumour testing (microsatellite instability (MSI) and/or immunohistochemistry (IHC) staining) to identify mutation carriers. DESIGN: Data from population-based or clinic-based patients with colorectal cancers enrolled through the Colon Cancer Family Registry were analysed. Evaluation included MSI, IHC and germline mutation analysis for MLH1, MSH2, MSH6 and PMS2. Personal and family cancer histories were used to calculate PREMM(1,2,6) predictions. Discriminative ability to identify carriers from non-carriers using the area under the receiver operating characteristic curve (AUC) was assessed. Predictions were based on logistic regression models for (1) cancer assessment using PREMM(1,2,6), (2) MSI, (3) IHC for loss of any MMR protein expression, (4) MSI+IHC, (5) PREMM(1,2,6)+MSI, (6) PREMM(1,2,6)+IHC, (7) PREMM(1,2,6)+IHC+MSI. RESULTS: Among 1651 subjects, 239 (14%) had mutations (90 MLH1, 125 MSH2, 24 MSH6). PREMM(1,2,6) discriminated well with AUC 0.90 (95% CI 0.88 to 0.92). MSI alone, IHC alone, or MSI+IHC each had lower AUCs: 0.77, 0.82 and 0.82, respectively. The added value of IHC+PREMM(1,2,6) was slightly greater than PREMM(1,2,6)+MSI (AUC 0.94 vs 0.93). Adding MSI to PREMM(1,2,6)+IHC did not improve discrimination. CONCLUSION: PREMM(1,2,6) and IHC showed excellent performance in distinguishing mutation carriers from non-carriers and performed best when combined. MSI may have a greater role in distinguishing Lynch syndrome from other familial colorectal cancer subtypes among cases with high PREMM(1,2,6) scores where genetic evaluation does not disclose a MMR mutation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".