Interpreting data in the face of competing explanations: assessing the hypothesis that observed spontaneous clearance of Helicobacter pylori was all measurement error
Bibliographic record
Abstract
BACKGROUND: We previously reported frequent transient positive urea breath tests for Helicobacter pylori infection in a cohort study of young children, and interpreted this as evidence of frequent spontaneous clearance of this infection. In a commentary, Perry and Parsonnet suggested that all transient positive tests we observed could be false positives and thus the appearance of transient infection could be an artifact. METHODS: We address the logic of the implicit argument that the transient infections were an artifact and we demonstrate a simple likelihood calculation to assess the plausibility of competing explanations. We calculate the likelihood of observing our data based on a range of clearance and measurement error rates and then how this updates a set of prior beliefs. RESULTS: The likelihood calculations and resulting posterior probabilities show strong support for the hypothesis of spontaneous clearance, after allowing for measurement error, even starting with a very high prior probability of no spontaneous clearance. The scenario Perry and Parsonnet present is incompatible with our data, and thus not a plausible explanation for our observations. Attributing most observed transient infections to measurement error requires assuming a high false positive rate and a very low infection rate and/or a high false negative rate, alternatives that are not supported by evidence. CONCLUSIONS: Acknowledgment of plausible levels of measurement error does not change the strong support our data provides for the hypothesis of frequent transient infection. Debate about competing explanations for observations should be accompanied by quantitative analysis that shows which is more plausible. We demonstrate one method for doing such analysis.
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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 | 0.280 | 0.719 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".