This is not Lynch syndrome: lessons from misattributed diagnoses in constitutional mismatch repair deficiency
Bibliographic record
Abstract
Background: Underdiagnosis of constitutional mismatch repair deficiency (CMMRD) syndrome leads to suboptimal cancer surveillance and management of CMMRD patients. Assessing pitfalls that led to the misdiagnosis of CMMRD is important to improve care trajectories, and to highlight the importance of accurate molecular and pathology-based assessment of patients presenting with CMMRD-associated features. Materials and methods: A retrospective chart review of two patients with molecularly confirmed CMMRD ascertained through the Medical Genetics service of the McGill University Health Centre (MUHC) was conducted to study the pathway and pitfalls to diagnosis. Records were reviewed and summarized as timelines to depict important events relating to diagnosis and management of CMMRD patients. Results: Unfamiliarity with CMMRD contributed to a diagnosis delay and initiation of CMMRD-specific surveillance. Pitfalls along the diagnostic pathway included inaccurate clinical information relayed to pathologists, unfamiliarity with CMMRD-defining features on immunohistochemistry (IHC) analyses, IHC variability and unreliability, and lack of awareness of the pivotal role for medical genetics in the diagnosis of CMMRD. Conclusions: Improved awareness of CMMRD in patients presenting with CMMRD-associated features can help guide IHC analysis and expedite referral to medical genetics for accurate molecular diagnosis. Consequently, timely CMMRD diagnosis improves surveillance and patient management and allows for appropriate genetic counseling for family members.
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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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 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".