Discussing current syphilis case definitions: A proposal for a “probable infectious” case
Bibliographic record
Abstract
Syphilis has increased in recent years, causing difficulties for clinicians and public health practitioners alike. While one issue with the current management of syphilis is that it has both a myriad of presentations and complicated laboratory results, another issue relates to the current case definitions used to define and track syphilis in public health surveillance. One item that is missing is a "probable" case definition, which could help capture the number of likely cases of syphilis that were appropriately treated clinically, but which failed to reach public health case definition based on serologic markers. This approach could produce a more accurate picture of the breadth of syphilis transmission in North America and help better appreciate the groups most affected by syphilis change. We put forward and argue this position herein.
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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 | 0.123 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.014 | 0.046 |
| Scholarly communication | 0.015 | 0.056 |
| Open science | 0.013 | 0.018 |
| Research integrity | 0.031 | 0.078 |
| Insufficient payload (model declined to judge) | 0.003 | 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".