Consumer Preference of Products for the Prevention and Treatment of Stretch Marks: Systematic Product Search
Bibliographic record
Abstract
Background Striae distensae, or stretch marks, are a common and distressing condition affecting females two-and-a-half times more frequently than males. Despite the numerous products available for stretch mark prevention and treatment, there have been few studies that consider consumer product preference. Objective The aim of this study was to determine which products were preferred by consumers for the prevention and treatment of stretch marks based on product vehicle and product ingredients. Methods In January 2020, a search was conducted on internet retailer Amazon for products related to stretch marks. The top products were identified as those with 100 reviews or greater and a rating of 4 or higher. The products were classified as either stretch mark–specific or non stretch mark–specific. Price, rating, type of vehicle, and specific ingredients of both product groups were compared. Vehicle-type and ingredients in both product groups were compared with two-tailed two-sample proportion tests to determine if certain vehicles or ingredients were more likely to be found in stretch mark–specific products. P<.05 indicated statistical significance. Results Out of over 10,000 products, 184 were selected as the top products according to the review and rating criteria of which 117 (63.6%) were stretch mark–specific and 67 (36.4%) were non stretch mark–specific. Oil was the most common vehicle (131/184, 71.2%) while vitamin E was the most common ingredient (58/184, 31.5%). Oil, as a vehicle, was more likely to be found in stretch mark–specific products than in non stretch mark–specific products (P=.001). Olive oil (P=.02) and cocoa butter (P=.08), Centella asiatica (P=.01), and shea butter (P=.003) were the ingredients more likely to be found in stretch mark–specific products than in non stretch mark–specific products. Conclusions This study demonstrated that there are many products available for the prevention and treatment of stretch marks and identified specific ingredients in the products preferred by customers. There are few studies investigating the effectiveness of the major ingredients in the stretch mark products that are preferred by consumers. Future studies can focus on the effectiveness of the ingredients found in the products that are preferred by consumers.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".