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Imaging in acute ischaemic stroke: pearls and pitfalls

2017· review· en· W2738518592 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.

Bibliographic record

VenuePractical Neurology · 2017
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsMedicineAcute strokeStroke (engine)Ischaemic strokeNeurologyNeuroimagingIntensive care medicineAngiographyWork-upNeuroradiologyRadiologyInternal medicineIschemiaTissue plasminogen activatorPsychiatry

Abstract

fetched live from OpenAlex

Prompt and accurate diagnosis is the foundation of acute ischaemic stroke care. Multiple positive endovascular thrombectomy trials in ischaemic stroke patients with large vessel occlusions have further emphasised this but also added complexity to treatment decisions. CT angiography is now routine for patients who present with an acute stroke syndrome around the world. Members of the neurology and stroke teams (rather than radiologists) are often the first doctors to lay eyes on the CT images and are best equipped to integrate the clinical picture with the imaging findings. A sound understanding of acute stroke imaging is therefore essential for clinicians who work with acute stroke patients. This review describes some pearls we have gleaned from our own experience in acute stroke imaging as well as some potential follies to be avoided.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.002
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 head0.079
GPT teacher head0.419
Teacher spread0.340 · 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