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Record W4387331497 · doi:10.1093/bjd/ljad361

Harmonization of outcomes in epidermolysis bullosa: report of the Core Outcome Sets for Epidermolysis Bullosa (COSEB) kick-off meeting

2023· article· en· W4387331497 on OpenAlexaff
Eva W H Korte, Phyllis I. Spuls, Peter C. van den Akker, Dimitra Kiritsi, Martin Laimer, Anna M.G. Pasmooij, Rainer Riedl, Elizabeth Vroom, Verena Wally, Tobias Welponer, Maria C. Bolling

Bibliographic record

VenueBritish Journal of Dermatology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsEpidermolysis bullosaHarmonizationMedicineOutcome (game theory)ComparabilityPoolingDermatologyComputer science

Abstract

fetched live from OpenAlex

The COSEB kick-off meeting was organized in April 2023, and highlighted the stakeholder perspectives on the unmet and urgent need to work towards reasonable harmonization of outcome measurement in epidermolysis bullosa (EB) by developing core outcome sets for the different EB types. Standardized and uniform outcome assessment holds great promise to reduce selective reporting, improve the comparability and pooling of treatment outcomes, and enhance the efficacy of future research in EB.

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 imitation

Not 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.

metaresearch head score (Codex)0.458
metaresearch head score (Gemma)0.296
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.542
Threshold uncertainty score0.668

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4580.296
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0070.006
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0030.013
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.143
GPT teacher head0.437
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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