Enhancing Patient Compliance for Usage of Laundry Detergents Free of Dyes and Perfumes: Potential Impact of Better Cleaning Performance
Bibliographic record
Abstract
Sensitive skin, an often self-reported condition, is characterized by an unpleasant sensory experience to a variety of consumer products. Certain ingredients in consumer products, such fragrances and dyes, are believed to exacerbate skin sensitivities. Due to an increased prevalence of people reporting sensitive skin, a variety of consumer products are formulated for people with this condition. A segment of commercially marketed laundry detergents, commonly known as free detergents, have been formulated without dyes and perfumes to accommodate skin sensitivities. In the US and Canada respectively, 80% and 97% of dermatologist recommend the use of free detergents for their patients with sensitive skin. However, consumers have expressed dissatisfaction with free detergents, with 39% reporting they are not satisfied with their free detergent’s cleaning performance. When people switch from the leading free laundry detergent, they will switch to a non-free detergent 60% of the time, going against dermatologist recommendations and potentially further aggravating their skin sensitivities. Recently, a survey of US households with sensitive skin showed that 98.8% said that they would be more likely to consistently use a detergent that cleans better. Herein are reported data showing Tide Pods Free & Gentle outperformed other free detergents in cleaning across a wide variety of laundry stains and in SEM visual analysis of soil residues on fibers. It is postulated that the better cleaning detergent may help drive patient compliance with dermatologist recommendations for usage of a free detergent for their patients with sensitive skin.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".