Family cancer histories predictive of a high risk of hereditary non‐polyposis colorectal cancer associate significantly with a genomic rearrangement in hMSH2 or hMLH1
Bibliographic record
Abstract
Hereditary non-polyposis colorectal cancer (HNPCC) results from inactivating germline mutations in a set of DNA-mismatch-repair genes, of which the most clinically relevant are hMSH2 and hMLH1. Computer-assisted pedigree risk assessment tools are available to assist in the calculation of an individual's likelihood of bearing such a deleterious mutation. One such tool, cancergene version 3.4 (http://www3.utsouthwestern.edu/cancergene) was used to assess the risk of a deleterious mutation in the genes hMSH2 and/or hMLH1 in a series of probands selected from a panel of 67 South-western Ontario kindred previously identified as likely candidates for HNPCC by established clinical criteria. A DNA sample isolated from peripheral blood leukocytes obtained from each of these probands was examined for genomic rearrangement using the multiplex ligation-dependent probe amplification (MLPA) method. Of the individuals calculated to have a risk of >50% of a hMSH2 or hMLH1 gene mutation by the CancerGene risk assessment tool, 69% (9/13) were shown to have a genomic rearrangement resulting in the deletion of one or more exons of one of these two genes. Family cancer histories predictive of a high risk of HNPCC significantly associate with a genomic rearrangement in hMSH2 or hMLH1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".