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Record W4229020621 · doi:10.1055/s-0042-1746079

Use of Neuroimaging Techniques in Glioma Patients – Results of an International Survey on behalf of the EORTC Brain Tumor Group

2022· article· en· W4229020621 on OpenAlexaff
Philipp Lohmann, Marion Smits, Evangelia Razis, Martin Köcher, Karl‐Josef Langen, Filip De Vos, Martin Bendszus, E. Franceschi, Anca‐Ligia Grosu, Inge Compter, D. Galanaud, Jaime Gállego Pérez de Larraya, Jens Gempt, Peter Hau, Nicolaus Andratschke, J. C. Tonn, Galareh Zadeh, Michael Weller, Matthias Preusser, Norbert Galldiks

Bibliographic record

VenueNuklearmedizin - NuclearMedicine · 2022
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsNeuroimagingGliomaBrain tumorModality (human–computer interaction)Medical imagingMedicineMedical physicsComputer scienceRadiologyArtificial intelligencePathologyPsychiatryCancer research

Abstract

fetched live from OpenAlex

Ziel/Aim Multimodal imaging offers the potential to provide valuable diagnostic information in brain tumor patients. Considering the increasing number and availability of advanced neuroimaging techniques, selecting and applying the best modality may be therefore difficult. The present survey was carried out to evaluate the preferred use of various neuroimaging applications in patients with glioma.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.298
Teacher spread0.263 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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