Comprehensive review on marketed products of skin creams
Bibliographic record
Abstract
Creams have been used for centuries for both cosmetic and medicinal purposes, offering a variety of benefits for the skin. Cosmetic creams are typically used for cleansing, beautifying, and moisturizing the skin, helping to maintain its appearance and health. In contrast, medicinal creams are formulated to treat and protect the skin from conditions such as infections caused by bacteria, fungi, or injuries. While the skin has its own natural healing capabilities, medicinal creams can significantly enhance the healing process, particularly for wounds, burns, and other skin ailments. These creams work by providing a protective barrier, reducing the risk of infection and promoting faster recovery. The formulation of creams involves various techniques, including the selection of appropriate ingredients and the careful blending of substances to achieve desired properties. Creams can be classified based on their intended purpose, with each type offering specific benefits. The evaluation of creams involves different metrics, such as texture, absorbency, and stability, to assess their effectiveness. Additionally, creams are made with a range of ingredients that serve various functions, from moisturizing to antimicrobial actions. Understanding the benefits and drawbacks of different types of creams is essential for their optimal use in skincare and medicinal applications.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".