Development of an algorithm to facilitate the clinical management of syphilis
Bibliographic record
Abstract
BACKGROUND: Syphilis staging is important to determine treatment, post-treatment monitoring, and sexual partner follow-up. Many prescribers find syphilis staging to be challenging. Current guidelines for the management of patients diagnosed with syphilis provide little direction aside from an overview of some common symptoms and directing providers to stage cases in conjunction with experienced colleagues. LOCAL PROBLEM: In Canada and the United States, the rate of infectious syphilis has increased noticeably since 2000. Given the increase in rates of syphilis, it is important for all clinicians to understand how to appropriately manage patient care to reduce rates of infection. METHODS AND INTERVENTIONS: A clinical algorithm was developed to stage infectious syphilis. This was tested among nurse practitioners and physicians in a sexually transmitted infection clinic. The algorithm was developed based on a review of the available United States, Canadian, and British practice guidelines. RESULTS: Project results demonstrated that this resource could be a relevant practice tool for providers in multiple clinical settings to ensure that patients receive appropriate diagnosis, staging, and treatment of syphilis infection. A case study of a patient who presented to the clinic as a contact is used to review the algorithm and demonstrate the appropriate clinical management of patients. CONCLUSIONS: The algorithm appropriately guided practice and was useful to clinicians.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".