Utility of Whole Genome Sequencing in Assessing and Enhancing Partner Notification of Neisseria gonorrhoeae Infection
Bibliographic record
Abstract
BACKGROUND: Gonorrhea is a sexually transmitted infection of global concern. We investigated whole-genome sequencing (WGS) as a tool to measure and enhance partner notification (PN) in gonorrhea management. METHODS: Between May and November 2018, all N. gonorrhoeae isolated from patients attending Leeds Sexual Health, United Kingdom, underwent WGS. Reports listing sequences within 20 single-nucleotide polymorphisms (SNPs) of study isolates within a database containing select isolates from April 1, 2016, to November 15, 2018, were issued to clinicians. The proportion of cases with a potential transmission partner identified by PN was determined from patient and PN data. The WGS reports were reviewed to identify additional cases within 6 SNPs or less and verified for PN concordance. RESULTS: Three hundred eighty isolates from 377 cases were successfully sequenced; 292 had traceable/contactable partners and 69 (18%) had a potential transmission partner identified by PN. Concordant PN and WGS links were identified in 47 partner pairs. Of 308 cases with no transmission partner by PN, 185 (60%) had a case within 6 SNPs or less; examination of these cases' PN data identified 7 partner pairs with previously unrecognized PN link, giving a total of 54 pairs; all had 4 or less SNP differences. The WGS clusters confirmed gaps in partner finding, at individual and group levels. Despite the clinic providing sexual health services to the whole city, 35 cases with multiple partners had no genetically related case, suggesting multiple undiagnosed infections. CONCLUSIONS: Whole-genome sequencing could improve gonorrhea PN and control by identifying new links and clusters with significant gaps in partner finding.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".