Bibliographic record
Abstract
OBJECTIVE: To provide an update on new diagnostic tests and antiviral strategies for managing genital herpes. QUALITY OF EVIDENCE: Treatment guidelines are based on randomized clinical trials and recommendations from the Expert Working Group on Canadian Guidelines for Sexually Transmitted Diseases. Recommendations concerning other aspects of managing genital herpes (e.g., indications for using type-specific serologic tests) are mainly based on expert opinion. MAIN MESSAGE: Genital herpes is one of the most common sexually transmitted diseases, affecting about 20% of sexually active people; up to 80% of cases are undiagnosed. Because of frequent atypical presentation and the emotional burden associated with genital herpes, clinical diagnosis should be confirmed by viral culture. Type-specific serologic assays are now available, but their use is often restricted to special situations and requires adequate counseling. New antivirals (valacyclovir and famciclovir) with improved pharmacokinetic profiles have now been approved for episodic treatment of recurrences and suppressive therapy. CONCLUSION: Wise use of new diagnostic assays for herpes simplex coupled with more convenient treatment regimens should provide better management of patients with genital herpes.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".