Importance of case age in the purported association between phylogenetics and hemolytic uremic syndrome in<i>Escherichia coli</i>O157:H7 infections
Bibliographic record
Abstract
Escherichia coli O157:H7 is the largest cause of hemolytic uremic syndrome (HUS). Previous studies proposed that HUS risk varies across the E. coli O157:H7 phylogenetic tree (hypervirulent clade 8), but the role of age in the association is unknown. We determined phylogenetic lineage of E. coli O157:H7 isolates from 1160 culture-confirmed E. coli O157:H7 cases reported in Washington State, 2004-2015. Using generalised estimating equations, we tested the association between phylogenetic lineage and HUS. Age was evaluated as an effect modifier. Among 1082 E. coli O157:H7 cases with both phylogenetic lineage and HUS status (HUS n = 76), stratified analysis suggested effect modification by age. Lineages IIa and IIb, relative to Ib, did not appear associated with HUS in children 0-9-years-old. For cases 10-59-years-old, lineages IIa and IIb appeared to confer increased risk of HUS, relative to lineage Ib. The association reversed in ⩾60-year-olds. Results were similar for clade 8. Phylogenetic lineage appears to be associated with HUS risk only among those ⩾10-years-old. Among children <10, the age group most frequently affected, lineage does not explain progression to HUS. However, lineage frequency varied across age groups, suggesting differences in exposure and/or early disease manifestation.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".