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Record W4324322487 · doi:10.7759/cureus.36194

Reversible CT Scan Hypodensity in Acute Ischemic Stroke Patient With Low Initial Alberta Stroke Program Early CT Score (ASPECTS) Following Endovascular Thrombectomy: A Case Report

2023· article· en· W4324322487 on OpenAlexaboutno aff
Mohammed S. Alqahtani, Naif F Alharbi, Bashayer G Alghamdi, Muhannad Asiri, Mohammed M Alwadai, Amani H Maghfuri, Saeed S Alzahrani

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)Ischemic strokeComputed tomographyAcute strokeRadiologySurgeryEmergency departmentCardiologyIschemia

Abstract

fetched live from OpenAlex

According to current American and European guidelines, mechanical thrombectomy is recommended only for patients with an Alberta Stroke Program Early CT Score (ASPECTS) of 6 or higher. However, recent literature suggests that the potential benefits of reperfusion therapy should not be solely determined by baseline ASPECTS. In this case report, we present a young female patient with a low initial ASPECTS (4-5), who underwent mechanical thrombectomy and showed marked improvement in both CT imaging and clinical symptoms. Our findings potentially show that mechanical thrombectomy may be beneficial even for patients with an initial ASPECTS ≤ 5. These results may contribute to the growing evidence supporting the use of mechanical thrombectomy as a viable treatment option for acute ischemic stroke patients with low baseline ASPECTS.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.270
Teacher spread0.255 · 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 designCase report
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

Citations1
Published2023
Admission routes1
Has abstractyes

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