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Proposed methodology and classification of Infarct in New Territory (INT) after endovascular stroke treatment

2015· review· en· W1772602517 on OpenAlexaff
Mayank Goyal, Bijoy K. Menon, Andrew M. Demchuk, Jeffrey L. Saver, Muneer Eesa, Charles B.L.M. Majoie, Mahesh Jayaraman, Michael D. Hill

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2015
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)AngiographyRadiologyAcute strokeOcclusionEndovascular treatmentComplicationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

While the overall complication rates for endovascular treatment for acute stroke has been extremely low in recent trials, it is important to separate out and accurately document complications. One of these complications that is usually related to the endovascular intervention is Infarct in New Territory (INT). We propose a standardized methodology for documenting INT after the procedure. This new classification takes into account variations in vascular anatomy and location of the occlusion. In addition, given that after the recent trials, vascular imaging (eg, CT angiography (CTA)) is now the standard of care in the work up of acute ischemic stroke, this classification utilizes the information on the pre-procedure non-invasive vascular imaging, the angiography images from end of procedure and the location of lesions on the follow-up scan.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
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 head0.200
GPT teacher head0.385
Teacher spread0.185 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2015
Admission routes1
Has abstractyes

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