Consideration of sexually transmitted infections in the differential diagnosis: Case studies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The rates of many sexually transmitted infections (STIs) have increased in recent years. Many health care professionals miss these potential diagnoses in clinical practice. METHODS: Two case studies are presented, one an adult female with dysuria; the other an adult male with a rash. Appropriate differential diagnoses and relevant history, examination, and investigation details are discussed. CONCLUSIONS: Not all dysuria signifies a urinary tract infection. Although most rashes are not syphilis or HIV, it is important to rule out these etiologies for rashes in adults without a previous history of similar dermatologic conditions. IMPLICATIONS FOR PRACTICE: Due to increased rates of many STI and HIV, it is important for nurse practitioners who work in primary care to consider these infections in patients who present with dysuria and rashes. Similarly, nurse practitioners who work in STI clinic settings should consider non-STI diagnoses in their work. In both cases, a perspective that includes both STI and non-STI etiologies is essential.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".