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Endovascular treatment for cerebral venous thrombosis: current status, challenges, and opportunities

2022· review· en· W4206736206 on OpenAlex
Mayank Goyal, Joachim Fladt, Jonathan M. Coutinho, Rosalie McDonough, Johanna M. Ospel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2022
Typereview
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineEndovascular treatmentVenous thrombosisStroke (engine)Intensive care medicineDiseaseThrombosisRadiologySurgeryInternal medicineAneurysm

Abstract

fetched live from OpenAlex

Cerebral venous thrombosis (CVT) mostly affects young people. So far, endovascular treatment (EVT) has not been shown to be beneficial in CVT, partially because venous EVT tools are not yet fully optimized, and therefore EVT is only used as a rescue treatment in rare cases. Identifying a subgroup of CVT patients that could benefit from EVT is challenging, given the milder course of disease compared with acute ischemic stroke, the paucity of data on prognostic factors (both in the clinical and imaging domain), and the lack of consensus on what constitutes 'technical success' in CVT EVT. In this review, we discuss the major obstacles that are encountered when trying to identify CVT patients that may benefit from EVT, and propose a roadmap that could help to overcome these challenges in the near future.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0010.000
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 head0.348
GPT teacher head0.382
Teacher spread0.033 · 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