Self-collected swabs of the urinary meatus diagnose more<i>Chlamydia trachomatis</i>and<i>Neisseria gonorrhoeae</i>infections than first catch urine from men
Bibliographic record
Abstract
OBJECTIVES: To compare first catch urine (FCU) and self-collected urinary meatal swabs for the detection of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) using the APTIMA Combo 2 assay. METHODS: A total of 511 young men from a high risk street youth clinic were studied. Group A (n=293) collected a FCU and a meatal APTIMA swab followed by Group B (n=218) who collected a FCU and two meatal samples using an APTIMA swab and a flocked swab. Order of sample collection was alternated. Individuals in Group B rated collection as easy, difficult or neither, then expressed a preference for sampling and swab type. All subjects performed meatal self-collection in the presence of a study monitor. RESULTS: The combined CT prevalence was 7.8% and 2.7% for NG where 80% of the men were without symptoms. Meatal swabbing identified 35 cases of CT and 14 cases of NG compared to 33 and 11 for FCU. Flocked and APTIMA swabs were equally effective in detecting more cases. The majority of men found self-collection of meatal swabs and urine to be easy. Although 63% preferred urine sampling, 60% of those who preferred swabbing selected the flocked swab. CONCLUSIONS: Collection of meatal swabs could serve as an alternative to urethral swabbing and FCU for the detection of CT and NG.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".