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Record W3149284698 · doi:10.1007/s00062-021-01012-6

Machine Outputs Must Be Checked

2021· article· en· W3149284698 on OpenAlexaboutno aff
Elias Kellner, Horst Urbach

Bibliographic record

VenueClinical Neuroradiology · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceReliability engineeringEngineering

Abstract

fetched live from OpenAlex

We read with interest the multicenter manuscript of Psychogios et al. in which they reported on the comparison of infarct core and tissue at risk maps generated by four different vendors as well as visual Cerebral Blood Volume-Alberta Stroke Program Early CT Score (CBV-ASPECTS) and visually assessed collateral scores They related the maps of 182 patients undergoing mechanical thrombectomy (MT) and receiving a TICI 2b, 2c or III reperfusion to the clinical outcome assessed with the modified Rankin score (mRS) and the functional disability defined as mRS > 2. They calculated mean differences between RAPID (iSchemaView Inc, Menlo Parc, CA, USA) and other software packages and illustrated them with Bland-Altman plots. They concluded that the infarct core defined by the RAPID software correlates best with the clinical outcome whilst VEOcore (VEObrain GmbH, Freiburg, Germany) and syngo.via (Siemens Healthineers AG, Erlangen, Germany) overestimate the infarct core and Olea (OLEA medical Inc., La Ciotat, France) underestimates it

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.083
GPT teacher head0.377
Teacher spread0.294 · 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.

Study designNot applicable
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

Citations6
Published2021
Admission routes1
Has abstractyes

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