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Record W4205912906 · doi:10.1017/cjn.2021.466

P.190 Choosing Endovascular Treatment or Thrombolysis in Patients with Pre-stroke Comorbidities: UNMASK EVT, a Worldwide Survey

2021· article· en· W4205912906 on OpenAlexaffvenue
A Ganesh, Nima Kashani, JM Ospel, AT Wilson, MM Foss, Gustavo Saposnik, MA Al-Mekhlafi, Mayank Goyal, BK Menon, Michael D. Hill

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)Toronto Public HealthCalgary Laboratory Services
Fundersnot available
KeywordsComorbidityOddsMedicineOdds ratioStroke (engine)ThrombolysisNeurologyNeuroradiologyInternal medicineLogistic regressionEmergency medicineMyocardial infarctionPsychiatry

Abstract

fetched live from OpenAlex

Background: Decisions to treat large-vessel occlusion with endovascular therapy(EVT) or intravenous alteplase depend on how physicians weigh benefits against risks when considering patients’ pre-stroke comorbidities. Methods: In an international survey, experts chose treatment approaches under current resources and under assumed ideal conditions for 10 of 22 randomly assigned case-scenarios. Five included comorbidities(metastatic/non-metastatic cancer, cardiac/respiratory/renal disease, non-disabling/mild cognitive impairment[MCI], physical dependence). We examined scenario/respondent characteristics associated with EVT/alteplase decisions using multivariable logistic regressions. Results: Among 607 physicians(38 countries), EVT was favoured in 1,097/1,379(79.6%) responses for comorbidity-related scenarios under current resources versus 1,510/1,657(91.1%,OR:0.38, 95%CI.0.31-0.47) for six “level-1A” scenarios (assuming ideal conditions:82.7% vs 95.1%,OR:0.25,0.19-0.33). However, this was reversed on including all other scenarios(e.g. under current resources:3,489/4,691[74.4%], OR:1.34,1.17-1.54). Responses favouring alteplase for comorbidity-related(e.g.75.0% under current resources) scenarios were comparable to level-1A scenarios(72.2%) and higher than all others(60.4%). No comorbidity-related factor independently diminished EVT-odds. MCI and dependence carried higher alteplase-odds; cancer and cardiac/respiratory/renal disease had lower odds. Relevant respondent characteristics included performing more EVT cases/year (higher EVT, lower alteplase-odds), practicing in East-Asia (higher EVT-odds), and in interventional neuroradiology(lower alteplase-odds vs neurology). Conclusions: Moderate-to-severe comorbidities did not consistently deter experts from EVT, suggesting equipoise about withholding EVT based on comorbidities. However, alteplase was often foregone when respondents chose EVT.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.033
GPT teacher head0.267
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes2
Has abstractyes

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