[Study on the relations between HLA-DRB 1 alleles and Helicobacter pylori infection].
Bibliographic record
Abstract
OBJECTIVE: In order to study the relation between human leukocyte antigen (HLA) DRB1 alleles and Helicobacter pylori (Hp) infection. METHODS: Hp-IgG antibody from 46 gastric cancer (GC), 75 esophageal cancer and 100 population-based controls were identified by Hp-IgG quantitative enzyme immunoassay. Biotest HLA-DRB enzyme linked probe hybridization assay kit (low resolution) was used to identify DRB1 alleles. RESULTS: (1) Frequency of DRB1 * 08 was significantly higher in Hp-IgG positive group than in Hp-IgG negative group (13.1% vs 4.4%, chi(2) = 11.14, P < 0.001). Frequency of DRB1 * 12 was significantly lower in Hp-IgG positive group than in Hp-IgG negatives (5.4% vs 11.3%, chi(2) = 4.49, P < 0.05). (2) Frequency of DRB1 * 02 in GC was significantly higher than that of controls. Frequency of DRB1 * 07 in GC was significantly lower than that of controls. However, neither the frequency of DRB1 * 02 between Hp-IgG positive and Hp-IgG negative groups nor the frequency of DRB1 * 07 between Hp-IgG positive and Hp-IgG negative groups showed significant differences in GC and controls. CONCLUSIONS: (1) HLA-DRB1 * 08 might serve a genetic risk factor for Hp infection while DRB1 * 12 might play a role of protecting effect against Hp infection. (2) DRB1 * 02 might be a genetic risk factor for GC while DRB1 * 07 might play a role of protecting effect against GC. However, the relations between DRB1 * 02, DRB1 * 07 and GC were not associated with Hp infection.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".