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Record W2891940995 · doi:10.1007/s13239-018-00376-0

Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): Phase I: Segmentation

2018· article· en· W2891940995 on OpenAlexaff
Philipp Berg, Samuel Voß, Sylvia Saalfeld, Gábor Janiga, Aslak W. Bergersen, Kristian Valen‐Sendstad, Jan Bruening, Leonid Goubergrits, Andreas Spuler, Nicole M Cancelliere, David A. Steinman, Vítor Mendes Pereira, Tin Lok Chiu, Anderson Chun On Tsang, Bong Jae Chung, Juan R. Cebral, Salvatore Cito, Jordi Pallarès, Gabriele Copelli, Benjámin Csippa, György Paál, Soichiro Fujimura, Hiroyuki Takao, Simona Hodis, Georg Hille, Christof Karmonik, Saba Elias, Kerstin Kellermann, Muhammad Owais Khan, Alison L. Marsden, Hernán G. Morales, Şenol Pişkin, Ender A. Finol, Mariya Pravdivtseva, Hamidreza Rajabzadeh-Oghaz, Nikhil Paliwal, Hui Meng, Santhosh Seshadhri, Matthew J. Howard, Masaaki Shojima, Shin‐ichiro Sugiyama, Kuniyasu Niizuma, S. V. Sindeev, С. В. Фролов, Thomas Wagner, Alexander Brawanski, Yi Qian, Yu-An Wu, Kent Carlson, Dan Dragomir‐Daescu, Oliver Beuing

Bibliographic record

VenueCardiovascular Engineering and Technology · 2018
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsUniversity of TorontoToronto Western HospitalUniversity Health Network
FundersBundesministerium für Bildung und ForschungDeutsche Forschungsgemeinschaft
KeywordsSegmentationAneurysmSphericityOstiumArtificial intelligenceSimilarity (geometry)Computer scienceRadiologyMedicineAnatomyComputer visionMathematicsImage (mathematics)SurgeryGeometry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.009
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0040.006
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0040.004

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 head0.010
GPT teacher head0.241
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations79
Published2018
Admission routes1
Has abstractno

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