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Variability of results of recent acute endovascular trials: a statistical analysis

2015· article· en· W2268152937 on OpenAlexaff
Mayank Goyal, Bijoy K. Menon

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of CalgaryFoothills Medical Centre
Fundersnot available
KeywordsMedicineNeurovascular bundleModified Rankin ScaleClinical trialStroke (engine)OcclusionInternal carotid arteryIntracerebral hemorrhageStentSurgeryRadiologyInternal medicineIschemiaSubarachnoid hemorrhageIschemic stroke

Abstract

fetched live from OpenAlex

Five recent trials have shown the benefit of endovascular treatment in patients with acute ischemic stroke due to large vessel occlusion in the anterior circulation.1–5 There were a lot of commonalities between the trials. The key ones were: most patients had clinically severe ischemic stroke; most patients had small core based on imaging; all patients had neurovascular imaging to detect the presence of proximal vessel occlusion; most patients had an M1±intracranial internal carotid artery occlusion; a stent retriever was used for clot retrieval in the majority of patients.6–8 There were also commonalities in the results: all trials used the modified Rankin Scale (mRS) at 90 days and used shift analysis (EXTEND-IA was a phase IIB study with reperfusion and/or NIH Stroke Scale at 24 h as the primary outcome; however this trial also reported mRS at 90 days as their secondary analysis); all trials showed a statistically significant benefit of endovascular treatment over the control arm. The complication rates (symptomatic intracranial hemorrhage) of endovascular treatment were exceedingly low across all trials. There were, nonetheless, some differences between these trials. The key differences were: some trials had a lot of focus on speed and workflow (ESCAPE and SWIFT PRIME), some trials used CT perfusion for patient selection (EXTEND-IA and most of the patients …

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)0.012
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
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 head0.116
GPT teacher head0.364
Teacher spread0.248 · 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 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

Citations5
Published2015
Admission routes1
Has abstractyes

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