Responsible Use of Doxycycline for Prevention of Sexually Transmitted Infections Includes Both Recognizing Its Benefits and Planning for Antimicrobial Resistance Monitoring
Bibliographic record
Abstract
To the Editor—We thank Manoharan-Basil and colleagues for their comments [1] on our recently published trial of doxycycline preexposure prophylaxis (doxyPrEP) for sexually transmitted infections (STIs) [2]. Quite justifiably, we share their concerns about antimicrobial resistance (AMR). This is a key evaluation parameter for studies in this field, including doxycycline postexposure prophylaxis (doxyPEP) trials [3–5]. In their letter, they assert that our article concludes that “the risk of doxyPrEP selecting for antimicrobial resistance is low.” This was not our conclusion, and we are pleased to be given an opportunity to clarify our position. Manoharan-Basil and colleagues [1] suggest that we base our assessment on 2 claims: (1) that there was no significant increase in Staphylococcus aureus resistance in our study's immediate arm and, (2) that there is no association between doxycycline and β-lactam resistance in S. aureus. In response we would reiterate, as they rightly point out, that our analysis showed no statistically significant difference in doxycycline-resistant S. aureus between study arms. In addition, despite these nonsignificant findings, we clearly state in our Discussion that “S. aureus from nasal samples showed a potential increase in doxycycline resistance over time”—acknowledging possible longitudinal changes within our trial's immediate doxyPrEP arm. Second, Manoharan-Basil and colleagues reanalyzed our S. aureus AMR data using slightly different parameters (ie, on vs off doxyPrEP) and did find a significant difference (P = .002). It is not surprising that different analytic methods may yield slightly different results, particularly with a low sample size. Regardless of this, we highlight in our article that doxycycline resistance may emerge during doxyPrEP.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".