A retrospective global study of the prevalence of O-serotypes of invasive <i>Escherichia coli</i> disease in patients admitted to tertiary care hospitals
Bibliographic record
Abstract
Abstract Invasive Escherichia coli disease (IED) is associated with high hospitalization and mortality rates, particularly among adults aged ≥60 years. O-antigens are virulence factors required for E. coli survival. To inform EXPEC9V development, a novel glycoconjugate vaccine targeting E. coli O-antigens that is no longer in active clinical development, this retrospective observational study describes O-serotype prevalence among E. coli isolates from IED patients. Eligible patients were identified from medical record databases (9 January 2018–8 November 2019) across 17 tertiary care hospitals in Europe, North America, and Asia. To estimate vaccine serotype coverage of EXPEC9V, E. coli isolates were O-serotyped using whole-genome sequencing and agglutination. Antimicrobial susceptibility testing was also performed. Nine hundred and two patients were enrolled, of whom 690 (76.5%) were aged ≥60 years. Common serotypes were O25, O2, O6, O1, O15, O75, O16, O4, and O18, with O25 being the most reported (17.3%). In patients aged ≥60 years, 422/637 E. coli isolates were EXPEC9V O-serotypes. EXPEC9V O-serotype prevalence did not substantially differ when stratified according to sex, presence of a positive blood culture, sepsis, fatality, or multidrug resistance. Consistent with previous studies, serotype O25 was most prevalent and associated with ~20% of cases. An EXPEC9V vaccine serotype coverage of 66.2% was observed for IED patients aged ≥60 years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".