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Record W2995364329 · doi:10.4103/aian.aian_434_19

Effect of age on arterial recanalization and clinical outcome in thrombolyzed acute ischemic stroke in CLOTBUST cohort

2019· article· en· W2995364329 on OpenAlexaff
Arvind Sharma, Vijay K. Sharma, Aftab Ahmad, Deepak Gupta, Khursheed A. Khan, Ashfaq Shuaib, Andrei V. Alexandrov, Maher Saqqur

Bibliographic record

VenueAnnals of Indian Academy of Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineThrombolysisModified Rankin ScaleOcclusionTranscranial DopplerBolus (digestion)Internal medicineUnivariate analysisSurgeryIschemiaIschemic strokeMultivariate analysisMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Despite the evidence from randomized clinical trials, the effectiveness of intravenous tissue recombinant plasminogen activator (IV-tPA) for elderly patients (≥80 yrs) with acute ischemic stroke (AIS) is often an important consideration in clinical practice. We evaluated the effect of older age on arterial recanalization, timing of recanalization and outcome in thrombolysed AIS patients. METHODS: Consecutive AIS patients treated with IV-tPA and transcranial Doppler (TCD) examination within 3 hours of symptom-onset were included. Thrombolysis in Brain Ischemia (TIBI) flow-grading system was used to interpret TCD findings of persistent occlusion, re-occlusion and complete recanalization within 2 hours of IV-tPA bolus. Poor functional outcome was defined by modified Rankin score of 3 or more. Univariate and multiple logistic regression analyses were performed to assess the effect of age on clinical and TCD outcome measures. RESULTS: = 0.008). CONCLUSION: Elderly AIS patients achieve relatively poor functional outcome after IV-tPA despite similar rates of arterial recanalization. However, there is no increase in the hemorrhagic risk. Perhaps, decision for IV thrombolysis in elderly patients should be made cautiously.

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 score0.010
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.378
Teacher spread0.339 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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