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Record W2003389943 · doi:10.3143/geriatrics.47.58

Efficacy of tissue plasminogen activator in older patients

2010· article· en· W2003389943 on OpenAlexaboutno aff
Shingo Mitaki, Satoshi Abe, Akira Shirasawa, Ryukichi Matsui, Genya Toyoda, Hirokazu Bokura, Shuhei Yamaguchi

Bibliographic record

VenueNippon Ronen Igakkai Zasshi Japanese Journal of Geriatrics · 2010
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTissue plasminogen activatorPlasminogen activatorInternal medicine

Abstract

fetched live from OpenAlex

AIM: To evaluate the efficacy, outcome, and side effects of tissue plasminogen activator for cerebral infarction in patients aged 75 years or older. METHODS: Subjects consisted of 30 patients who had been treated with tissue plasminogen activator between October, 2005 and March 2009, in Shimane University Hospital. We divided the patients into two groups: those less than 75 years old and those 75 years old and older, and evaluated the pattern of disease, therapeutic efficacy, side effects of bleeding, and factors affecting the modified Rankin Scale on discharge. RESULTS: There was no significant difference between groups in the improvement level of NIH Stroke Scale (p=0.66), but modified Rankin Scale 2 or lower patients on discharge were significantly fewer (p=0.02). Multivariate analysis found that age was a factor in significant outcome deterioration (p=0.04, OR1.2). In the older patient group, there were significantly more unfavorable outcomes with anterior infarction. However, there was no significant difference between groups in outcome in patients with ASPECTS-DWI (Alberta Stroke Programme Early CT Score-Diffusion Weight Imaging) > or =8. There was no difference in the rate of hemorrhagic side effect between the two groups. CONCLUSION: We can expect effects similar to those in patients younger than 75 years if the ischemic lesions of older patients are narrow when coming to the hospital.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.259
Teacher spread0.250 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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