Seroprevalence of Helicobacter Pylori Infection in Patients with Lymphoma
Bibliographic record
Abstract
To determine the Helicobacter pylori (HP) seroprevalence in patients with non-Hodgkin's lymphoma (NHL) and other hematological conditions. Sera were collected from 444 patients with NHL, Hodgkin's disease (HD), lymphoproliferative disorders (LPD), myeloproliferative disorders (MPD), and other hematological conditions. HP seropositivity was determined by ELISA and the results were compared among diagnostic groups HP seropositivity was observed in 168/444 (38%) of the total population. Higher seropositivity rates were associated with increasing age (p=0.001), and country of birth outside the USA and Canada (p=0.0001). Among the diagnostic groups, patients with NHL demonstrated the highest frequency (43%) and those with HD, the lowest frequency (20%; p=.026) of HP seropositivity. The differences among diagnostic groups remained statistically significant after controlling for country of birth (p<0.05), but not after controlling for patient age at diagnosis. The HP seroprevalence of G1 NHL was 55% compared to 40% for non-G1 NHL (p=NS). The highest rate of HP seropositivity (67%) occurred in gastric MALT lymphoma patients, although this did not reach statistical significance compared to the non MALT group (50%) due to small sample size. In conclusion, the rate of HP seropositivity in patients with MALT lymphoma in the USA appears to be lower than in Europe. Helicobacter pylori does not appear to be an important factor in other types of NHL of the G1 tract or elsewhere. Studies of HP prevalence should be controlled for country of birth as well as for age.
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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) | 0.000 | 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 | 0.000 | 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".