MétaCan
Menu
Back to cohort
Record W2174657578 · doi:10.1002/ana.24558

Tranexamic acid–associated seizures: Causes and treatment

2015· review· en· W2174657578 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnals of Neurology · 2015
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSt. Michael's HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoInternational Anesthesia Research Society
KeywordsTranexamic acidMedicineAnesthesiaEpilepsySurgeryPsychiatryBlood loss

Abstract

fetched live from OpenAlex

Antifibrinolytic drugs are routinely used worldwide to reduce the bleeding that results from a wide range of hemorrhagic conditions. The most commonly used antifibrinolytic drug, tranexamic acid, is associated with an increased incidence of postoperative seizures. The reported increase in the frequency of seizures is alarming, as these events are associated with adverse neurological outcomes, longer hospital stays, and increased in-hospital mortality. However, many clinicians are unaware that tranexamic acid causes seizures. The goal of this review is to summarize the incidence, risk factors, and clinical features of these seizures. This review also highlights several clinical and preclinical studies that offer mechanistic insights into the potential causes of and treatments for tranexamic acid-associated seizures. This review will aid the medical community by increasing awareness about tranexamic acid-associated seizures and by translating scientific findings into therapeutic interventions for patients.

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.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

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

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 head0.276
GPT teacher head0.422
Teacher spread0.147 · 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