Factors associated with rectal pH among men who have sex with men
Bibliographic record
Abstract
Background Rectal chlamydia treatment failures up to 22% with azithromycin 1 g have been reported, but low tissue concentrations are unlikely to be the cause. Anecdotally, low rectal pH could reduce rectal azithromycin concentrations, with in vitro studies reporting higher minimum inhibitory concentrations (MICs) with lower pHs for antibiotics used to treat sexually transmissible infections (STIs). Leucocytes arising from an inflammatory immune response could also lower pH and efficacy. We examined factors that may alter rectal pH and potentially influence treatment outcomes. METHODS: We recruited consecutive men who have sex with men (MSM) from a Dutch STI clinic between October 2016 and July 2018 who had not used antibiotics in the past fortnight. Rectal mucus collected under anoscopy using a cotton swab was used to wet a pH indicator strip. Logistic regression was used to examine the association of pH <8.0 to demographic, dietary, sexual health and behaviour data, recent medication use and STI diagnosis. RESULTS: In total, 112 MSM were recruited (median age 37 years). It was found that 45% and 39% of men were HIV positive or had a rectal infection, respectively. And 50% had a rectal pH <8.0, with 27% reporting a pH between 6.0 and 6.5 where treatment failure is thought to occur for azithromycin. The adjusted odds ratio (OR) of a pH <8.0 showed that being aged 36-45 years (OR 6.7; 95%CI: 1.9-23.4) or having high rectal leucocytes in a Gram smear (OR 0.3; 95%CI: 0.1-0.7) were significantly associated with a low and high rectal pH, respectively. CONCLUSIONS: Lower rectal pH among MSM is associated with older age and could influence the rectal pharmacokinetics of azithromycin and other drugs influenced by pH and may therefore affect treatment outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".