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Record W2966798102 · doi:10.1097/tme.0000000000000254

Choosing the Correct “-ase” in Acute Ischemic Stroke

2019· article· en· W2966798102 on OpenAlexaff
George Dillon, Stacie Stevens, Wendy Dusenbury, L Massaro, Florence Toy, Barbara Purdon

Bibliographic record

VenueAdvanced Emergency Nursing Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsDillon Consulting
Fundersnot available
KeywordsTenecteplaseReteplaseMedicineTissue plasminogen activatorThrombolytic drugStroke (engine)Myocardial infarctionFibrinolytic agentPulmonary embolismIntensive care medicineThrombolysisInternal medicine

Abstract

fetched live from OpenAlex

Alteplase is a tissue plasminogen activator approved for treating acute ischemic stroke (AIS), acute myocardial infarction (AMI), and acute massive pulmonary embolism. Two additional tissue plasminogen activators, tenecteplase and reteplase, are also approved for AMI treatment. However, neither tenecteplase nor reteplase is approved for AIS treatment. The U.S. Food and Drug Administration has received reports of accidental administration of tenecteplase or reteplase instead of alteplase in patients with AIS, which can lead to potential overdose. Primary factors contributing to medication errors include use of the abbreviations "TPA," "tPA," or "TNK" in written or verbal orders and use of these agents in similar settings. Steps to reduce the likelihood of accidental substitution include use of full brand or generic names and inclusion of the indication in written and verbal orders, addition of alerts in automated dispensing machines and ordering systems, and use of stroke boxes containing alteplase and materials for administration.

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)0.003
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0160.012

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 head0.009
GPT teacher head0.294
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations15
Published2019
Admission routes1
Has abstractyes

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