Evaluation of a population‐based approach to familial colorectal cancer
Bibliographic record
Abstract
As Newfoundland has the highest rate of familial colorectal cancer (CRC) in the world, we started a population-based clinic to provide colonoscopic and Lynch syndrome (LS) screening recommendations to families of CRC patients based on family risk. Of 1091 incident patients 51% provided a family history. Seventy-two percent of families were at low or intermediate-low risk of CRC and colonoscopic screening recommendations were provided by letter. Twenty-eight percent were at high and intermediate-high risk and were referred to the genetic counsellor, but only 30% (N = 48) were interviewed by study end. Colonoscopy was recommended more frequently than every 5 years in 35% of families. Lower family risk was associated with older age of proband but the frequency of screening colonoscopy recommendations varied across all age groups, driven by variability in family history. Twenty-four percent had a high MMR predict score for a Lynch syndrome mutation, and 23% fulfilled the Provincial Program criteria for LS screening. A population-based approach in the provision of colonoscopic screening recommendations to families at risk of CRC was limited by the relatively low response rate. A family history first approach to the identification of LS families was inefficient.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".