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Record W4281919354 · doi:10.51731/cjht.2022.351

Systemic Thrombolysis by Alteplase for Acute Ischemic Stroke

2022· article· en· W4281919354 on OpenAlexaff
Nazia Darvesh, Robyn Butcher

Bibliographic record

VenueCanadian Journal of Health Technologies · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsCanadian Agency for Drugs and Technologies in Health
Fundersnot available
KeywordsMedicineThrombolysisStroke (engine)Significant differencePediatricsInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex


 Evidence was summarized to determine the effect of alteplase in adult stroke patients.
 There is substantial uncertainty concerning the evidence due to the risk of bias in the available studies and imprecision in how the magnitude of the treatment effects were estimated.
 The identified research suggests that alteplase administered within 3 hours of a stroke might result in:
 
 fewer deaths after 18 months and little-to-no difference in death after 7 days, 3 months, 6 months, or 3 years
 increased brain bleeds after 7 days but no difference after 36 hours or after 3 months
 improvements in functioning and independence after 7 days and after 6 months; at 3 months, some studies showed no difference in independence and another study showed higher functioning.
 
 
 The identified research suggests that alteplase administered between 3 hours and 4.5 hours after a stroke might result in:
 
 little-to-no difference in deaths after 3 months; at 7 days, some evidence showed little-to-no difference in death while other evidence suggested more deaths
 little-to-no differences in brain bleeds after 36 hours; at 7 days, some evidence showed no effect on brain bleeds, while other evidence showed more brain bleeds
 no differences in functioning and independence after 6 months; at 3 months, some evidence showed no effect on functioning, while other evidence reported improved functioning.
 
 

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.284
Teacher spread0.256 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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