101 Increasing Public Search Interest in Vaginal Laser Therapy: Changes Over Time and Geography
Bibliographic record
Abstract
Changes such as vaginal atrophy can impact sexual function with substantial impact on quality of life. Laser therapy is a unique treatment for atrophy, avoiding hormones. Although no data exists, vaginal lasers are promoted for the treatment of urinary incontinence. There is ongoing research as well as public debate on the efficacy and safety of vaginal laser therapy (VLT). To assess online search trends of health information relating to VLT. Google Trends was queried for the term “vaginal laser” to assess relative search volume (RSV) from October 2013 to October 2018. This was compared to “vaginal rejuvenation,” “laser vaginal rejuvenation,” and “vaginal laser for incontinence.” The quality of information available on the internet was assessed using Health On the Net Foundation (HON) certification. The online search data show a trend towards increasing volume for the term “vaginal laser” from 2013 to 2018 The greatest RSV for that search term was during the period of February 2017 (Figure 1), coinciding with a widely publicized showcase held by a well-known cosmetic gynecologic surgeon. Surprisingly, there was not a large spike around the time of the July 30, 2018 FDA safety communication on the use of vaginal lasers. The top 5 US states with the greatest RSV for vaginal laser were Florida (100), Georgia (87), California (69), Texas (68), and New York (67). When compared to related terms, “vaginal rejuvenation” was the most common (25) compared to “vaginal laser” (10), “vaginal laser rejuvenation” (2), and “vaginal laser incontinence” (0). Worldwide the countries with the greatest search volume for vaginal laser were Australia (100), the United States (89), Spain (69), Canada (67) and New Zealand (51). Quality assessment showed only 5% of vaginal laser search Results were HON certified.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".