Improved diagnostic yield of neuromuscular disorders applying clinical exome sequencing in patients arising from a consanguineous population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Neuromuscular diseases (NMDs) include a broad range of disorders affecting muscles, nerves and neuromuscular junctions. Their overlapping phenotypes and heterogeneous genetic nature have created challenges in diagnosis which calls for the implementation of massive parallel sequencing as a candidate strategy to increase the diagnostic yield. In this study, total of 45 patients, mostly offspring of consanguineous marriages were examined using whole exome sequencing. Data analysis was performed to identify the most probable pathogenic rare variants in known NMD genes which led to identification of causal variants for 33 out of 45 patients (73.3%) in the following known genes: CAPN3, Col6A1, Col6A3, DMD, DYSF, FHL1, GJB1, ISPD, LAMA2, LMNA, PLEC1, RYR1, SGCA, SGCB, SYNE1, TNNT1 and 22 novel pathogenic variants were detected. Today, the advantage of whole exome sequencing in clinical diagnostic strategies of heterogeneous disorders is clear. In this cohort, a diagnostic yield of 73.3% was achieved which is quite high compared to the overall reported diagnostic yield of 25% to 50%. This could be explained by the consanguineous background of these patients and is another strong advantage of offering clinical exome sequencing in diagnostic laboratories, especially in populations with high rate of consanguinity.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it