Bibliographic record
Abstract
OBJECTIVE: To summarize and determine the appropriate use for the new and old management tools for genital warts. SOURCES OF INFORMATION: The following databases were searched: MEDLINE, PubMed, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, ACP Journal Club, and Trip. The bibliographies of retrieved papers were also reviewed. Clinical trials, qualitative review articles, consensus reports, and clinical practice guidelines were retrieved. MAIN MESSAGE: Symptomatic warts are prevalent in at least 1% of the population between the ages of 15 and 49, with estimates of up to 50% of the population being infected with human papillomavirus at some point in their lifetime. Imiquimod and podophyllotoxin are 2 new treatments for external genital warts that are less painful and can be applied by patients at home. In addition, the quadrivalent human papillomavirus vaccine has been shown to be efficacious in preventing genital warts and cervical cancer. There is still a role for the older treatment methods in certain situations, such as intravaginal, urethral, anal, or recalcitrant warts; or for pregnant patients. CONCLUSION: The new treatments of external genital warts can reduce the pain of treatment and the number of office visits. Other treatment methods are still useful in certain situations.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".