Barriers and Facilitators in Diagnostic Pathways That Align Universal Tumor Screening and Mainstream Genetic Testing for Lynch Syndrome in Colorectal Cancer: Protocol for a Scoping Review With a Narrative Synthesis
Bibliographic record
Abstract
BACKGROUND: Approximately 3% of colorectal cancers (CRCs) are due to Lynch syndrome (LS), a hereditary cancer syndrome caused by pathogenic variants (PVs) in the mismatch repair (MMR) genes. Patients with CRC and LS have elevated lifetime risks for a range of cancers and require personalized treatment and targeted surveillance. Relatives of people affected by LS who share the same PV also have elevated cancer risks and can benefit from preventive measures and/or risk-reducing surgeries. Despite this, LS remains vastly underdiagnosed. Universal tumor screening (UTS) for deficient MMR is recommended in diagnosing LS in patients with CRC. This process, when combined with genetic testing (GT) offered within routine cancer care (termed "mainstream GT"), aims to identify individuals at risk efficiently, but integrating UTS and mainstream GT for LS in CRC is a complex endeavor. OBJECTIVE: The aim of the proposed scoping review will be to comprehensively explore the literature on diagnostic pathways comprising UTS and mainstream GT for LS among patients with CRC and barriers and facilitators in their implementation. METHODS: The scoping review will follow Arksey and O'Malley's expanded framework. Results will be reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and summarized quantitatively. A narrative synthesis will also be performed using the Theoretical Domains Framework. RESULTS: The results will be presented in a forthcoming scoping review, which we expect to publish in a peer-reviewed journal by early 2026. CONCLUSIONS: Aligning UTS with mainstream GT for LS in CRC may boost early diagnosis and prevention while reducing waiting times and other patient burdens. By addressing barriers to and facilitators in diagnostic pathways, health care systems can improve the identification and management of LS, ultimately leading to better outcomes for patients and their families. The insights gained from this scoping review will inform the development of a mixed methods study about implementing diagnostic pathways for LS in CRC that integrate UTS and mainstream GT in Italy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/70831.
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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 | 0.103 | 0.127 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".