Helicobacter pylori infection in Arctic Indigenous communities: assessing the validity of infection status measures and estimating the effect of bacterial load on gastritis severity
Bibliographic record
Abstract
Helicobacter pylori (Hp) causes chronic inflammation of the stomach lining, also known as gastritis, in nearly all people with a persistent infection. Chronic inflammation of the stomach lining can lead to atrophy of stomach glands, a condition known as atrophic gastritis, believed to be the start of a sequence of pathological conditions associated with an increased risk of stomach cancer. The Canadian North Helicobacter pylori (CANHelp) Working Group conducted community projects to respond to concerns of Arctic Indigenous communities about health risks from Helicobacter pylori (Hp) infection. CANHelp community projects used multiple methods for classifying Hp infection, including the urea breath test (UBT), pathological evaluation of stomach tissue, and tissue culture. My thesis assessed the validity of Hp infection detection methods and estimated the effect of the Hp bacterial load on the severity of gastritis among CANHelp project participants. Estimating the accuracy of alternative staining methods provides information needed to evaluate the cost-effectiveness of special stains for detecting Hp. In this thesis, I investigated whether using special stains (Giemsa, Warthin-Starry and/or immunohistochemical (IHC) stains) on tissue slides for pathological assessment of stomach tissue biopsies improves the accuracy of Hp detection. To do this, I analyzed data from participants with stomach tissue biopsies collected from 2008 through 2013 in 4 CANHelp community projects. I estimated accuracy measures (agreement, sensitivity, specificity, and predictive values) for each stain’s classification of tissue samples as Hp-positive or Hp-negative compared against different definitions of the ‘gold standard’ classification. I also compared the Hp density gradings of 2 pathologists and assessed the relationship between Hp density and UBT values. The special stains evaluated for this study had similar accuracy based on estimated measures. Because IHC has a much higher cost than the other stains assessed here, either Warthin-Starry or Giemsa would be more cost-effective than IHC for improving Hp detection during the pathological assessment of stomach tissue biopsies relative to not using a special stain. The Hp density grades of the 2 pathologists showed good consistency of agreement. The Hp density grades were positively associated with UBT values. Considering the high global mortality from stomach cancer, a 2014 International Agency for Research on Cancer Hp Working Group recommended that countries explore the possibility of introducing Hp screening and treatment programs; this panel cautioned, however, that decisions about whether and how to implement such programs must be based on local considerations of disease burden and other factors of relevance to health policy. Identifying local indicators of high risk for targeted interventions is key for effective cancer prevention programs. Thus, understanding the relationship between Hp density and gastritis severity in specific populations at elevated risk of stomach cancer has great public health significance. In this thesis, I also investigated whether the frequency of severe Hp-associated gastritis increases as Hp density increases. To do this, I estimated the effect of Hp density on gastritis severity among Hp-positive participants in CANHelp community projects. This analysis used data from participants with stomach tissue biopsies collected from 2008 through 2017 in 7 CANHelp community projects. The results showed that gastritis severity increases sharply with increasing Hp density among Hp-positive participants.
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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 | 0.042 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".