International assessment of Lynch syndrome screening practices
Bibliographic record
Abstract
Lynch syndrome (LS) is an inherited cancer syndrome that increases risk of developing certain cancers, most commonly colorectal and endometrial. It is important to distinguish between LS and sporadic cancers because it can help inform the risk of additional cancers as well as identify relatives who may also be at risk of developing LS-associated cancers. In the US, there has been considerable effort by organizations including the Lynch Syndrome Screening Network (LSSN) to promote universal tumor screening as part of routine diagnosis of colon and endometrial cancers to identify more cases of LS. However, it is unclear what LS screening practices currently exist internationally. In order to characterize global LS screening practices with regards to screening of colorectal cancer tumors, this study implemented a survey to capture screening procedures and methods, cascade testing, and efficacy measures. The online survey was distributed to individuals at member organizations of LSSN, members of the International Society for Gastrointestinal Hereditary Tumors (InSiGHT), and the Collaborative Group of the Americas on Inherited Gastrointestinal Cancer (CGA-IGC). Additionally, two interview guides were developed depending on whether survey respondents’ organizations had a routine LS screening program or not. This was done by creating open-ended questions based on survey questions. This will support future work in elucidating how barriers and facilitators, such as insurance and social factors, may inform LS screening practices. There were 27 survey respondents from seven countries: Canada, US, Denmark, Ireland, Netherlands, UK, and Japan. Some key findings from the survey showed that no institutions offer only direct-to-germline testing, 92.6% of responding institutions perform universal tumor screening, 81.5% of institutions use a patient-mediated method of informing relatives about cascade testing, and 48.1% of institutions systematically track LS detection rates. The public health significance of this project is that it identifies ways in which resources developed by LSSN could be leveraged to facilitate implementation of LS screening programs (especially universal tumor screening) world-wide and provide guidance on how population-specific improvements could be made to current screening programs. Ultimately, this will help to increase LS diagnoses and reduce the burden that LS can have in families globally.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".