Pathologic <i>RFC1</i> repeat expansions do not contribute to the development of inflammatory neuropathies
Bibliographic record
Abstract
Abstract Biallelic expansions of the AAGGG repeat in the replication factor C subunit 1 (RFC1) have recently been described to be responsible for cerebellar ataxia, peripheral neuropathy and vestibular areflexia syndrome. This genetic alteration has also allowed genetic classification in up to one-third of cases with idiopathic sensory neuropathy. Here, we screened a well-characterized cohort of inflammatory neuropathy patients for RFC1 repeat expansions to explore whether RFC1 was increased from background rates and possibly involved in the pathogenesis of inflammatory neuropathy. A total of 259 individuals with inflammatory neuropathy and 243 healthy controls were screened for the AAGGG repeat expansion using short-range flanking PCR and repeat-primed PCR. Cases without amplifiable PCR product on flanking PCR and positive repeat-primed PCR were also tested for the mostly non-pathogenic expansions of the AAAGG and AAAAG repeat units. None of the patients showed biallelic AAGGG expansion of RFC1, and their carrier frequency for AAGGG was comparable with controls [n = 27 (5.2%) and n = 23 (4.7%), respectively; P > 0.5]. Data suggest that the pathologic expansions of AAGGG repeats do not contribute to the development of inflammatory neuropathies nor lead to misdiagnosed cases. Accordingly, routine genetic screening for RFC1 repeat expansion is not indicated in this patient population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".