MétaCan
Menu
Back to cohort
Record W3015701520 · doi:10.2196/18295

Consumer Preference of Products for the Prevention and Treatment of Stretch Marks: Systematic Product Search

2020· article· en· W3015701520 on OpenAlex
Pengyi Zhu, Andrew Fung, Benjamin K.P. Woo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Dermatology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProduct (mathematics)PreferenceIngredientAdvertisingMarketingBusinessFood scienceMathematicsStatisticsChemistry

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.117

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.270
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it