Evaluating the impact of universal Lynch syndrome screening in a publicly funded healthcare system
Bibliographic record
Abstract
PURPOSE: Referrals for Lynch syndrome (LS) assessment have traditionally been based on personal and family medical history. The introduction of universal screening practices has allowed for referrals based on immunohistochemistry tests for mismatch repair (MMR) protein expression. This study aims to characterize the effect of universal screening in a publicly funded healthcare system with comparison to patients referred by traditional criteria, from January 2012 to March 2017. METHODS: Patient files from the time of initiation of universal screening from 2012 to 2017 were reviewed. Patients were sorted into two groups: (a) universally screened and (b) referred by traditional methods. Mutation detection rates, analysis of traditional testing criteria met, and cascade carrier testing were evaluated. RESULTS: The mutation detection rate of the universal screening group was higher than the traditionally referred group (45/228 (19.7%) vs 50/390 (12.5%), P = .05), though each were able to identify unique patients. An analysis of testing criteria met by each patient showed that half of referred patients from the universal screening group could not meet any traditional testing criteria. CONCLUSION: The implementation of universal screening in a publicly funded system will increase efficiency in detecting patients with LS. The resources available for genetic testing and counseling may be more limited in public systems, thus inclusion of secondary screening with BRAF and MLH1 promoter hypermethylation testing is key to further optimizing efficiency.
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 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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".