Bibliographic record
Abstract
Background: Globally, New Zealand has one of the highest mortality and incidence rates of MND (1), yet no systematic large-scale genetic studies have been undertaken to determine whether there is a genetic basis for this high rate of MND.Objectives: Over the last 5 years, we sought to identify the prevalence of genetic variants in known MND-linked genes in New Zealand patients with MND.A total of 184 participants were enrolled; 149 with MND (128 sporadic, 21 familial) and 35 unaffected but who are at risk of familial MND.Methods: Participants were initially screened for the 4 most common MND genes using either Sanger sequencing (SOD1, TARDBP, FUS) or repeat-primed PCR (C9orf72).Variants previously implicated in MND were then examined on Illumina SNP microarrays, covering an additional 18 untargeted genes.For participants with familial or young-onset MND (<35 y) not explained by these tests, we used the Invitae MND þ FTD þ Alzheimers gene panel covering 42 genes.Results: Thirty three of the 184 participants (18.5%) were found to carry known pathogenic variants; the majority had a pathogenic C9orf72 hexanucleotide repeat expansion (24/ 184), and the remainder had previously reported pathogenic genetic variants in SOD1 (p.Glu101Gly, p.Ile114Thr, 9/185).Of these 33, 6 (18.2%) enrolled in the study with sporadic MND, and 16 (48%) as pre-symptomatic unaffected individuals.Fourteen variants of uncertain significance were also identified of which one; DCTN1 c.279 þ 1G > C, is predicted to be a pathogenic splice variant and is the subject of ongoing study.Discussion: Given the number of pre-symptomatic carriers identified, our study supports the notion that genetic screening services should be free for all New Zealanders with MND and that at-risk family members could benefit from genetic testing regardless of apparent inheritance pattern, particularly as gene-specific clinical trials are becoming available.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".