Bibliographic record
Abstract
PURPOSE OF REVIEW: Although the recommended treatment for syphilis is penicillin, azithromycin has been used as an alternative. We discuss azithromycin-related treatment failures and resistance in Treponema pallidum, and propose ways to meet the resulting clinical and public health challenges. RECENT FINDINGS: Azithromycin treatment failures in syphilis were first noted in San Francisco in 2002 and result from an A-->G mutation at position 2058 of the 23S rRNA gene of T. pallidum. This mutation confers resistance by precluding macrolide binding to the bacterial 50S ribosomal subunit, of which 23S rRNA is a structural component. Azithromycin resistance has also been identified in T. pallidum specimens from elsewhere in the United States, Ireland, and Canada, and the amount of resistant specimens has increased with time. Treatment with azithromycin or other macrolides appears to be a risk factor for presenting with a resistant T. pallidum strain. SUMMARY: Although T. pallidum remains sensitive to penicillin and certain other antibiotics, azithromycin resistance in T. pallidum has emerged and is increasing in the United States, Canada, and Ireland. This poses clinical and public health challenges, and indicates a need for further antibiotic drug development and surveillance for resistance in T. pallidum. If azithromycin is used to treat syphilis, clinicians and public health practitioners should remain vigilant for treatment failures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".