Influencing Itch: Why Training Background of Skinfluencers Matters from a Contact Allergen Perspective
Bibliographic record
Abstract
Dermatitis®Ahead of Print LetterInfluencing Itch: Why Training Background of Skinfluencers Matters from a Contact Allergen PerspectiveClaire Herzog, Hadley Johnson, Rob L. Shaver, and Sara A. HylwaClaire Herzoghttps://orcid.org/0009-0008-1016-438XUniversity of Minnesota Medical School, Minneapolis, MN, USA.Search for more papers by this author, Hadley Johnsonhttps://orcid.org/0000-0002-0101-2017University of Minnesota Medical School, Minneapolis, MN, USA.Search for more papers by this author, Rob L. ShaverUniversity of South Dakota Sanford School of Medicine, Sioux Falls, SD, USA.Search for more papers by this author, and Sara A. HylwaPark Nicollet Contact Dermatitis Clinic, Minneapolis, MN, USA.Department of Dermatology, University of Minnesota, Minneapolis, MN, USA. Search for more papers by this authorEmail the corresponding author at [email protected]Published Online:11 Jul 2023https://doi.org/10.1089/derm.2023.0078AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Influencing Itch: Why Training Background of Skinfluencers Matters from a Contact Allergen Perspective." Dermatitis®, , pp. FiguresReferencesRelatedDetails Volume 0Issue 0 Information© 2023 American Contact Dermatitis Society. All Rights Reserved.To cite this article:Claire Herzog, Hadley Johnson, Rob L. Shaver, and Sara A. Hylwa.Influencing Itch: Why Training Background of Skinfluencers Matters from a Contact Allergen Perspective.Dermatitis®.ahead of printhttp://doi.org/10.1089/derm.2023.0078Online Ahead of Print:July 11, 2023PDF download
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".