Dermatology on Instagram
Bibliographic record
Abstract
The novel photo-sharing social networking platform, Instagram, has an impressive following of 75 million daily users, with a predominantly younger and female demographic. This study investigated the presence of dermatology-related content on Instagram. The most popular professional dermatological organizations, dermatology journals, and dermatology related patient advocate groups on Facebook and Twitter, determined from a prior study, were searched for established profiles on Instagram. In addition, dermatology-related terms (i.e. dermatology, dermatologist, alopecia, eczema, melanoma, psoriasis, and skin cancer) and dermatology-related hashtags (i.e. #dermatology, #dermatologist, #melanoma, #acne, #psoriasis, and #alopecia) were searched. None of the top ten dermatological journals or professional dermatological organizations were found on Instagram. Although only one of the top ten patient advocate groups related to dermatology conditions, Melanoma Research Foundation, had an Instagram presence, there were many private offices, cosmetic products, and some patient advocacy groups. This novel social networking platform could grant dermatology journals and other professional organizations a unique opportunity to reach younger demographic populations, particularly women, with the potential for true educational and life-changing impact.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.134 | 0.041 |
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".