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Record W1981080514 · doi:10.1111/ane.12005

Neurologic safety event rates in the SENTIS trial control population

2012· article· en· W1981080514 on OpenAlexaff
Helmi L. Lutsep, Irfan Altafullah, Robin Roberts, Isaac E Silverman, Mark Turco, Anand Vaishnav

Bibliographic record

VenueActa Neurologica Scandinavica · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineStroke (engine)Adverse effectRandomized controlled trialClinical trialPopulationAnesthesiaCerebral edemaInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Adverse event (AE) rates for interventional stroke trials are not well established. AIMS: We prospectively evaluated control arm AEs from a randomized stroke trial to establish expected rates of neurologic AEs. METHODS: Control data from the Safety and Efficacy of NeuroFlo Technology in Ischemic Stroke (SENTIS) Trial were evaluated. Patients were ≥ 18 years with National Institutes of Health Stroke Scale (NIHSS) scores 5-18 within 14 h of stroke onset. Follow-up was 90 days. Neurological AEs and serious AEs (SAEs) were adjudicated and the following defined times used to determine treatment relatedness: 24-h imaging for intracranial hemorrhage (ICnH) including hemorrhagic transformation, 7 days each for cerebral edema and neurologic worsening/stroke progression, and 30 days for new ischemic strokes. RESULTS: The control group included 257 patients, 49.4% female, mean age of 68.3 years, and median NIHSS of 10. Neurologic AEs occurred at the following rates: ICnH 27.6%, cerebral edema 6.6%, neurologic worsening 18.3%, and new stroke 4.7%. Most of these events occurred within the defined times: ICnH 74.6%, cerebral edema 94.1%, neurologic worsening 87.2%, and new stroke 83.3%. CONCLUSIONS: SENTIS Trial control arm neurologic events provide estimates of expected AE rates and defined times that can be used for future stroke trial's safety assessments.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.294
Teacher spread0.273 · 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 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

Citations3
Published2012
Admission routes1
Has abstractyes

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