Who cares for syphilis? A cross-sectional study on diagnosis and treatment of syphilis by GPs in Amsterdam, the Netherlands
Bibliographic record
Abstract
BACKGROUND: Syphilis is a re-emerging infection. Sexually transmitted infection (STI) clinics and GPs are important providers of STI care in the Netherlands. The role of GPs in syphilis care is assumed to be small, since most men who have sex with men (MSM) visit STI clinics for STI care. AIM: To explore the role of GPs in the diagnosis and treatment of syphilis. DESIGN & SETTING: Data on syphilis diagnostics by GPs in Amsterdam between 2011 and 2017 were retrieved from laboratories, covering 90% of the GPs. The study also used the academic GPs' network database to explore the management of syphilis by GPs between 2013 and 2018. METHOD: Syphilis tests requested by GPs were analysed and compared with annual reports of the STI clinic. Patients with an International Classification of Primary Care-1 (ICPC-1) syphilis code were identified in the GP database. Cases diagnosed by the GP were evaluated whether they were treated by the GP or referred to secondary care. RESULTS: In the laboratory database, GPs had diagnosed syphilis 522 times, compared with 2515 times by the STI clinics. Based on the 90% coverage of GPs, the contribution of all Amsterdam's GPs was 19% of the total number of diagnoses. Consequently, the annual incidence of syphilis diagnosed by the GP was 10.2 per 100 000 inhabitants. Of the 43 cases identified in the GP database, six (14.0%) were referred and 33 (76.7%) were treated by a GP. CONCLUSION: Although for an individual GP, syphilis is rare to diagnose, GPs in Amsterdam do contribute to the rate of syphilis diagnosis and appear to treat the majority of cases that they have diagnosed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".