[Association between mutations of SCN9A gene and pain related to Parkinsonism].
Bibliographic record
Abstract
OBJECTIVE: To screening mutations of exons 15, 18 and 26 of sodium channel Nav1.7 (SCN9A) gene, and to assess its association with pain related to Parkinsonism. METHODS: Respectively, 101 patients with primary Parkinson's disease (PD) and 104 similar-aged volunteers without PD were recruited from March, 2008 to January, 2011. Mutations of above 3 exons in SCN9A gene was detected with PCR and direct sequencing. For 100 patients with Parkinsonism, the pain was scored with a McGill pain rating scale. Statistical analysis was performed with SPSS. RESULTS: The prevalence of pain in Parkinsonian was 57%. 43.86% patients with pain were males, and 56.14% were females. Based on Chaudhuri criteria, the pain symptoms may be classified as musculoskeletal pain (10.52%), radicular pain (10.52%), dyskinesis pain (54.38%), pain from akathisia and restlessness (14.04%), dyskinesis combined with radicular pain (5.26%), skeletal muscles pain and headache (1.75%), and arthralgia (3.50%). Two missense mutations were identified, which included 2794A/C (0.941/0.059) (rs12478318) (M932L) in exon 15 and 3448C/T (0.988/0.012) (rs6746030) (R1150W) in exon 18. The wild type A/C for the 2794 locus had a higher prevalence in PD patients with pain, but this was not statistically different. All of the 5 heterozygotes for 3448 (C/T) were found in Parkinsonian patients with pain. No homozygotes were found. CONCLUSION: The prevalence of pain was higher in Parkinsonian patients than general population, and the proportion of males to females was similar. More patients have suffered dyskinesis pain. A 3448 (C/T) mutation of SCN9A gene may be related to pathogenesis of pain in Parkinsonism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".