Patch Testing Interrupted: Virtual Patch Test Readings during the COVID-19 Pandemic
Bibliographic record
Abstract
DermatitisVol. 31, No. 4 LettersPatch Testing Interrupted: Virtual Patch Test Readings During the COVID-19 PandemicHarriet S. ChengHarriet S. ChengDepartment of Dermatology, Auckland District Health Board, Auckland, New Zealand [email protected]Search for more papers by this authorPublished Online:1 Aug 2020AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 31Issue 4Aug 2020 Information© 2020 American Contact Dermatitis Society. All Rights Reserved.To cite this article:Harriet S. Cheng.Patch Testing Interrupted: Virtual Patch Test Readings During the COVID-19 Pandemic.Dermatitis.Aug 2020.e35-e36.http://doi.org/10.1097/DER.0000000000000628Published in Volume: 31 Issue 4: August 1, 2020 TopicsCOVID-19 PDF 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".