Assessing the value of linking public health microbiology data to the UK Biobank
Bibliographic record
Abstract
Abstract Infection is important both as a cause of communicable diseases and as an exposure predisposing to non-communicable diseases. Investigating disease risk is a major research focus in large cohorts like UK Biobank. Linking cohorts to electronic health records, like the UK Health Security Agency’s Second Generation Surveillance System (SGSS), can enhance infection research. SGSS collects infection data from ∼200 microbiology laboratories across England, supporting surveillance, outbreak detection, and antimicrobial resistance monitoring. We previously described algorithms linking SGSS to UK Biobank and demonstrated their utility during the COVID-19 pandemic. Here, we assess the value of SGSS for infection research by comparing it to Hospital Episode Statistics (HES), a centralized clinical dataset on hospital admissions already available in UK Biobank. Genome-wide association studies (GWAS) were used to evaluate the performance of SGSS microbiological diagnoses versus HES diagnostic codes for identifying infection outcomes. SGSS contained substantially more infection records than HES by participant (82,888 vs 18,054), particularly for bacteria (excepting Helicobacter pylori and Mycobacterium tuberculosis ). SGSS yielded more GWAS hits (31 vs 12) encompassing more pathogens (12 vs 8). Our findings demonstrate the high scientific added-value of SGSS above and beyond that of HES, supporting its integration in UK Biobank for future infection research.
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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 | 0.093 | 0.425 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".