Reducing Discomfort and Irritation Related to Surgical Loop Masks in the Era of COVID-19: A Quality Improvement Initiative
Bibliographic record
Abstract
DermatitisVol. 32, No. 6 LettersReducing Discomfort and Irritation Related to Surgical Loop Masks in the Era of COVID-19: A Quality Improvement InitiativeSurav M. Sakya, Ryan M. Svoboda, Yesul Kim, and Alexandra FlammSurav M. SakyaAddress reprint requests to Surav M. Sakya, BS, 80 University Manor, East Hershey, Hershey, PA 17033 E-mail Address: [email protected].Penn State College of Medicine, Hershey, PA.Search for more papers by this author, Ryan M. SvobodaDepartment of Dermatology, Penn State Milton S. Hershey Medical Center, PA.Search for more papers by this author, Yesul KimDepartment of Dermatology, Penn State Milton S. Hershey Medical Center, PA.Search for more papers by this author, and Alexandra FlammDepartment of Dermatology, Penn State Milton S. Hershey Medical Center, PA.Search for more papers by this authorPublished Online:1 Dec 2021AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View article"Reducing Discomfort and Irritation Related to Surgical Loop Masks in the Era of COVID-19: A Quality Improvement Initiative." Dermatitis, 32(6), pp. e145–e146FiguresReferencesRelatedDetails Volume 32Issue 6Dec 2021 Information© 2020 American Contact Dermatitis Society. All Rights Reserved.To cite this article:Surav M. Sakya, Ryan M. Svoboda, Yesul Kim, and Alexandra Flamm.Reducing Discomfort and Irritation Related to Surgical Loop Masks in the Era of COVID-19: A Quality Improvement Initiative.Dermatitis.Dec 2021.e145-e146.http://doi.org/10.1097/DER.0000000000000719Published in Volume: 32 Issue 6: December 1, 2021PDF 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 | 0.040 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".