Case Report of a Human Papillomavirus Infection Treated with Green Tea Extract and Curcumin Vaginal Compounded Medications.
Bibliographic record
Abstract
The human papillomaviruses are the most common sexually transmitted infections in the U.S., and the majority of these infections are cleared by the body's natural immune system without causing any harm. The high-risk human papillomavirus types, however, cause approximately 5 percent of all cancers worldwide. A prophylactic vaccine was recently introduced, but there is still no commercially available treatment that targets active infections. This case study discusses a 48-year-old female who was diagnosed as human papillomavirus positive (+) following a routine gynecological examination. Upon discussion with a compounding pharmacist, the obstetrician/gynecologist prescribed Green Tea Extract 15% Vaginal Cream and Curcumin 250 mg Vaginal Suppositories, to be applied on alternate days for a period of 3 months. After only 1 month of treatment, the gynecologic cytology report tested negative for human papillomavirus. The patient was very satisfied with the compounded medications prescribed (treatment satisfaction questionnaire) and the human papillomavirus (+) diagnosis was reported to have only little/moderate impact on the patient's sexual life (Human Papillomavirus Impact Profile questionnaire). The successful results obtained in this case study confirm the beneficial properties of green tea extract and curcumin against the viruses, and highlight the key role of pharmaceutical compounding in current therapeutics.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".