Dual molecular diagnoses in a neurometabolic specialty clinic
Bibliographic record
Abstract
Reports of patients with concomitant diagnoses of two inherited genetic disorders, sometimes referred to as "double trouble," have appeared intermittently in the medical literature. We report eight additional cases with dual diagnoses of two genetic conditions. All cases had a phenotype atypical for their primary diagnosis, leading to the search for a second genetic diagnosis. These cases highlight the importance of the history, physical examination and continued work-up if the phenotype of the patient falls drastically outside what has been reported with their primary diagnosis. Some of the diagnoses of the patients presented here (e.g., Myotonic Dystrophy Type 1, fascioscapulohumeral muscular dystrophy) would not have been identified by genetic testing done on a next generation sequencing backbone (e.g., panel or exome sequencing). When the clinical picture is atypical or more severe than expected the possibility of a dual diagnosis (double trouble) should be considered. Identification of a second genetic condition can impact management and genetic counseling.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".