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Record W4395036406 · doi:10.1055/s-0044-1784028

Eye-Tracker-basierte Differenzierung von Schwindelursachen: Eine mobile Möglichkeit der schnelleren und genaueren Triagierung im Notfall

2024· article· de· W4395036406 on OpenAlexaff
Nadia Sadok, Lorenz Peters, Stephan Lang, Jan Egger, Dagny Holle, Moritz Meyer, Jens Kleesiek

Bibliographic record

VenueLaryngo-Rhino-Otologie · 2024
Typearticle
Languagede
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Einleitung Akuter Schwindel beeinträchtigt die Lebensqualität in besonderer Weise. Die Ursachen sind vielfältig und reichen von peripheren vestibulären Störungen bis hin zu lebensbedrohlichen zentralen Störungen wie Kleinhirninfarkten. Die schnelle Identifikation der zugrundeliegenden Die Ursache von Schwindel ist entscheidend für eine angemessene und zeitnahe Behandlung.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0050.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.006

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 head0.026
GPT teacher head0.312
Teacher spread0.286 · 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 designOther design
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

Citations0
Published2024
Admission routes1
Has abstractyes

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