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Record W4391873534 · doi:10.1093/jcag/gwad061.164

A164 OPTIMIZING A PROVINCIAL SCREENING PROGRAM FOR DETECTION OF LYNCH SYNDROME

2024· article· en· W4391873534 on OpenAlexaffabout
R Winter, Yujie Liu, James Stone, Heidi Rothenmund, B.N. Chodirker, C Kim, Julianne Klein, Harminder Singh

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLynch syndromeComputer scienceMedicineInternal medicineCancer

Abstract

fetched live from OpenAlex

Abstract Background Up to 30% of those with colorectal cancer (CRC) have an affected family member. Lynch syndrome (LS) is the most common inherited condition predisposing patients to CRC. LS is an autosomal dominant condition associated with microsatellite instability (MSI) and mutations in DNA mismatch repair (MMR) genes, and screening CRC cases ≤ age 70 can identify LS in a cost-effective manner. Manitoba launched Canada's first provincial reflex screening program for all CRC cases diagnosed ≤ age 70 using MMR immunohistochemistry (MMR-IHC) in 2017. Our prior 2022 study evaluating the program showed compliance rates of 89.4% for MMR-IHC reflex testing and a 75.8% overall referral rate of screen-positive cases to genetics. However, only 50% were referred directly by pathologists. Several modifications have since been implemented to enhance the program. These included providing feedback to pathologists, creating an “additional testing comment section” on synoptic pathology reports, and regular review of the pathology reports by a pathology assistant. Information materials were developed to encourage genetic testing among relatives. Aims Assess whether modifications to Manitoba’s universal screening protocol improved LS patient outcomes and determine any remaining factors to optimize. We specifically aimed to identify current compliance rates for MMR-IHC testing, the overall referral rate of screen-positive cases to genetics, uptake of genetic referrals and genetic testing as well as rate of cascade testing among relatives. Methods Data has been obtained by searching the provincial pathology database for “adenocarcinoma” in the colorectal specimen pathology reports. All specimens from 2022 and Q1/Q2 of 2023 were reviewed, meeting the criteria of: a) specimen from colon biopsy/excision containing the term “adenocarcinoma”, b) patient age ≤70 years, and c) missing MMR centralized by the IT/Pathology department. Additionally, a random sample of cases categorized as “MMR performed” will be audited. We are in the process of reviewing genetic referrals, genetic uptake and the frequency of reminders triggered to each pathologist. Results Currently, a total of 1,308 colonic adenocarcinoma specimens (811 from 2022, 497 for 2023) have been reviewed. Appropriate MMR testing was missed in only 1.5% of cases (13/811 =1.6% missed in 2022, 7/497= 1.4% missed in 2023). Conclusions Current MMR testing rates for colonic adenocarcinoma specimens within the Manitoba LS screening program have reached a current compliance rate of 98.5%, a substantial improvement from the prior audit (89.4% previously). Further data collection and analysis is ongoing and will also be presented. The reported rates of compliance are the highest that have been reported from any jurisdiction. This study highlights the sustained efforts required to improve detection of LS. Funding Agencies None

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.011
GPT teacher head0.260
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

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