Is the Utilization of Helicobacter pylori Stool Antigen Tests Appropriate in an Urban Canadian Population?
Bibliographic record
Abstract
OBJECTIVES: Helicobacter pylori stool antigen test (HpSAT) appropriateness was investigated by assessing its testing and positivity rates in Calgary, Canada. METHODS: The laboratory information system was accessed for all patients who received an HpSAT in 2018. Testing volume, test results, age, and sex of patients were collected. Sociodemographic risk factors and geospatial analysis were performed by matching laboratory data to the 2016 census data. Testing appropriateness was defined as a concordance between testing and positivity rates for each sociodemographic variable. RESULTS: In 2018, 25,518 H pylori stool antigen tests were performed in Calgary, with an overall positivity rate of 14.7%. Geospatial mapping demonstrated significant distribution variations of testing and positivity rates of HpSAT in the city. Certain sociodemographic groups studied (eg, recent immigrants) appeared to be appropriately tested (testing rate relative risk [RR] = 2.26, positivity rate RR = 4.32; P < .0001), while other groups (eg, male) may have been undertested (testing rate RR = 0.85, positivity rate RR = 1.14; P < .0001). CONCLUSIONS: Determining concordance of testing and positivity rate of a laboratory test can be used for assessing testing appropriateness for other diseases in other jurisdictions. This study demonstrated some at-risk patients may be missed for H pylori testing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".