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Record W2807814493 · doi:10.1055/s-0043-125304

Standardisierter Computer-basiert-organisierter Report des EEG (SCORE) – Eine strukturierende Form der EEG-Befundung

2018· article· de· W2807814493 on OpenAlexaff
Friedhelm C. Schmitt, Harald Aurlien, Jan Brøgger, Lawrence J. Hirsch, Donald L. Schomer, Eugen Trinka, Ronit Pressler, Richard Wennberg, Gerhard H. Visser, Monika Eisermann, Beate Diehl, Ronald P. Lesser, Peter W. Kaplan, S The Tich, J Lee, Hermann Stefan, M. Y. Neufeld, Guido Rubboli, Martin Fabricius, Elena Gardella, Daniella Terney, Pirgit Meritam, Tom Eichele, Eishi Asano, F. M. Cox, W. van Emde Boas, Rūta Mameniškienė, Petr Marusič, Jana Zárubová, I. Rosén, Anders Fuglsang‐Frederiksen, Akio Ikeda, David B. MacDonald, Kiyohito Terada, Yoshikazu Ugawa, Dong Zhou, Susan T. Herman, Sándor Beniczky

Bibliographic record

VenueKlinische Neurophysiologie · 2018
Typearticle
Languagede
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsElectroencephalographyPsychologyNeuroscienceAudiologyMedicine

Abstract

fetched live from OpenAlex

Zusammenfassung Eine 2013 von der „International Federation of Clinical Neurophysiology“ gegründete Taskforce hat eine international konsensfähige EEG-Terminologie entwickelt. Im Folgenden soll das Resultat – die 2. Version des Standardized Computer-based Organized Reporting of EEG (SCORE) - vorgestellt werden. Die Terminologie wurde im Rahmen eines Softwarepaketes (SCORE-EEG) in der klinischen Praxis an über 12.000 EEGs getestet. Die Auswahl der Begriffe ist kontextabhängig: die initiale Auswahl bestimmt, welche weiteren Auswahlmöglichkeiten zur Verfügung stehen. Im Verlauf wird automatisch ein Befund erstellt und dessen Einzelmerkmale in eine Datenbank eingespeist. SCORE verfügt über Module spezifisch für die Befundung epileptischer Anfälle, sowie charakteristischer neonataler und intensivmedizinische EEG-Merkmale. SCORE ist nicht nur ein nützliches Werkzeug im ambulanten, klinischen und wissenschaftlichen Setting, es erleichtert auch Qualitätssicherung, Datenaustausch und die EEG-Aus und Weiterbildung.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.003

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 head0.049
GPT teacher head0.300
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2018
Admission routes1
Has abstractyes

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