A survey of clinicians regarding preferred severity assessment tools for hidradenitis suppurativa
Bibliographic record
Abstract
International Journal of DermatologyVolume 60, Issue 6 p. e248-e251 Correspondence A survey of clinicians regarding preferred severity assessment tools for hidradenitis suppurativa Rob L. Shaver BS, orcid.org/0000-0003-0726-8758 School of Medicine, University of Minnesota-Twin Cities, Minneapolis, MN, USASearch for more papers by this authorGregor B. E. Jemec MD, DMSc, orcid.org/0000-0002-0712-2540 Department of Dermatology, Zealand University Hospital, Roskilde, Denmark Department of Dermatology, University of Copenhagen, Copenhagen, DenmarkSearch for more papers by this authorRebecca Freese MS, Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USASearch for more papers by this authorAfsaneh Alavi MD, MSc, Division of Dermatology, Department of Medicine, Women's College Hospital, Toronto, Ontario, Canada Division of Dermatology, Department of Medicine, University of Toronto, Toronto, Ontario, CanadaSearch for more papers by this authorMichelle A. Lowes MD, PhD, The Rockefeller University, New York, NY, USASearch for more papers by this authorNoah Goldfarb MD, Corresponding Author gold0414@umn.edu Departments of Medicine and Dermatology, University of Minnesota, Minneapolis, MN, USA Departments of Medicine and Dermatology, Minneapolis Veteran Affairs Health Care System, Minneapolis, MN, USASearch for more papers by this author Rob L. Shaver BS, orcid.org/0000-0003-0726-8758 School of Medicine, University of Minnesota-Twin Cities, Minneapolis, MN, USASearch for more papers by this authorGregor B. E. Jemec MD, DMSc, orcid.org/0000-0002-0712-2540 Department of Dermatology, Zealand University Hospital, Roskilde, Denmark Department of Dermatology, University of Copenhagen, Copenhagen, DenmarkSearch for more papers by this authorRebecca Freese MS, Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USASearch for more papers by this authorAfsaneh Alavi MD, MSc, Division of Dermatology, Department of Medicine, Women's College Hospital, Toronto, Ontario, Canada Division of Dermatology, Department of Medicine, University of Toronto, Toronto, Ontario, CanadaSearch for more papers by this authorMichelle A. Lowes MD, PhD, The Rockefeller University, New York, NY, USASearch for more papers by this authorNoah Goldfarb MD, Corresponding Author gold0414@umn.edu Departments of Medicine and Dermatology, University of Minnesota, Minneapolis, MN, USA Departments of Medicine and Dermatology, Minneapolis Veteran Affairs Health Care System, Minneapolis, MN, USASearch for more papers by this author First published: 12 November 2020 https://doi.org/10.1111/ijd.15295 Conflict of interest: None. Funding source: None. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume60, Issue6June 2021Pages e248-e251 RelatedInformation
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".