Germline mismatch repair (MMR) gene analyses from English NHS regional molecular genomics laboratories 1996–2020: development of a national resource of patient-level genomics laboratory records
Bibliographic record
Abstract
OBJECTIVE: To describe national patterns of National Health Service (NHS) analysis of mismatch repair (MMR) genes in England using individual-level data submitted to the National Disease Registration Service (NDRS) by the NHS regional molecular genetics laboratories. DESIGN: Laboratories submitted individual-level patient data to NDRS against a prescribed data model, including (1) patient identifiers, (2) test episode data, (3) per-gene results and (4) detected sequence variants. Individualised per-laboratory algorithms were designed and applied in NDRS to extract and map the data to the common data model. Laboratory-level MMR activity audit data from the Clinical Molecular Genetics Society/Association of Clinical Genomic Science were used to assess early years' missing data. RESULTS: Individual-level data from patients undergoing NHS MMR germline genetic testing were submitted from all 13 English laboratories performing MMR analyses, comprising in total 16 722 patients (9649 full-gene, 7073 targeted), with the earliest submission from 2000. The NDRS dataset is estimated to comprise >60% of NHS MMR analyses performed since inception of NHS MMR analysis, with complete national data for full-gene analyses for 2016 onwards. Out of 9649 full-gene tests, 2724 had an abnormal result, approximately 70% of which were (likely) pathogenic. Data linkage to the National Cancer Registry demonstrated colorectal cancer was the most frequent cancer type in which full-gene analysis was performed. CONCLUSION: The NDRS MMR dataset is a unique national pan-laboratory amalgamation of individual-level clinical and genomic patient data with pseudonymised identifiers enabling linkage to other national datasets. This growing resource will enable longitudinal research and can form the basis of a live national genomic disease registry.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".