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Record W3031257658 · doi:10.1097/md.0000000000020200

Successful dabigatran reversal after subdural hemorrhage using idarucizumab in a mobile stroke unit

2020· article· en· W3031257658 on OpenAlexafffund
Sibi Thirunavukkarasu, Hayrapet Kalashyan, Glen C. Jickling, Thomas Jeerakathil, Harsha Kamble Jayaprakash, Brian Buck, Ashfaq Shuaib, Kenneth Butcher

Bibliographic record

VenueMedicine · 2020
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Alberta Hospital
FundersUniversity Hospital FoundationUniversity of Alberta
KeywordsIdarucizumabMedicineDabigatranIntracerebral hemorrhageSubdural haematomaHematomaStroke (engine)WarfarinAnticoagulantAtrial fibrillationIntensive care medicineAnesthesiaSurgeryInternal medicineGlasgow Coma Scale

Abstract

fetched live from OpenAlex

RATIONALE: Idarucizumab is a specific reversal agent for patients with bleeding related to the anticoagulant dabigatran. There are no prior descriptions of Idarucizumab administration in the prehospital setting for intracranial hemorrhage. PATIENT CONCERNS: An 82-year-old woman treated with dabigatran for atrial fibrillation developed acute focal weakness. This led to activation of emergency medical services and assessment in the mobile stroke unit (MSU). DIAGNOSIS: Computed tomography of the brain performed in the MSU revealed an acute subdural hematoma. INTERVENTIONS: The patient was treated with Idarucizumab in the MSU. OUTCOMES: The subdural hematoma was treated with a burr hole evacuation and the patient was discharged to a rehabilitation facility without residual focal neurological deficits. LESSONS: Idarucizumab can be used safely and effectively to treat dabigatran-associated intracranial hemorrhage in the prehospital setting.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.341
Teacher spread0.265 · 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.

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

Citations9
Published2020
Admission routes2
Has abstractyes

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