Bibliographic record
Abstract
Adherence to topical treatment for a variety of chronic skin conditions, such as psoriasis, acne, and atopic dermatitis, is known to be very poor. A number of factors contribute to this phenomenon, including lack of treatment efficacy and patient concerns regarding side effects, among others. At the forefront of barriers facing optimal patient adherence to topical treatment is choosing the ideal topical vehicle formulation. Medical professionals have demonstrated a lack of understanding with respect to the composition and nomenclature of the various topical formulations. In this review, we clarify the properties and definitions of the following topical formulations: ointments, creams, lotions, topical solutions, topical suspensions, gels, foams, and sprays. We also provide suggested areas of application for the aforementioned formulations and a succinct, patient-geared summary of their advantages and disadvantages. This review hopes to deliver a clear and concise practical summary of the most commonly encountered topical formulations for the treatment of cutaneous disease for medical professionals. This will allow clinicians to provide their patients with accurate information to facilitate informed choice of topical formulation, which may serve to improve treatment adherence.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.045 |
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".