An association analysis of HLA-DQB1 with narcolepsy without cataplexy and idiopathic hypersomnia with/without long sleep time in a Japanese population
Bibliographic record
Abstract
Narcolepsy without cataplexy (NA w/o CA) (narcolepsy type 2) is a lifelong disorder characterized by excessive daytime sleepiness and rapid eye movement (REM) sleep abnormalities, but no cataplexy. In the present study, we examined the human leukocyte antigen HLA-DQB1 in 160 Japanese patients with NA w/o CA and 1,418 control subjects. Frequencies of DQB1*06:02 were significantly higher in patients with NA w/o CA compared with controls (allele frequency: 16.6 vs. 7.8%, P=1.1×10(-7), odds ratio (OR)=2.36; carrier frequency: 31.3 vs. 14.7%, P=7.6×10(-8), OR=2.64). Distributions of HLA-DQB1 alleles other than DQB1*06:02 were compared between NA w/o CA and narcolepsy with cataplexy (NA-CA) to assess whether the genetic backgrounds of the two diseases have similarities. The distribution of the HLA-DQB1 alleles in DQB1*06:02-negative NA w/o CA was significantly different from that in NA-CA (P=5.8×10(-7)). On the other hand, the patterns of the HLA-DQB1 alleles were similar between DQB1*06:02-positive NA w/o CA and NA-CA. HLA-DQB1 analysis was also performed in 186 Japanese patients with idiopathic hypersomnia (IHS) with/without long sleep time, but no significant associations were observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".