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E-064 ASPECTS ≥5 on Non-Contrast CT and CT Angiography Source Images Predicts Clinical Outcome in a Cohort of 30 Patients Undergoing Mechanical Thrombectomy with Stent-Retrievers

2013· article· en· W2319673045 on OpenAlexaboutno aff
Josser E Delgado Almandoz, Yasha Kadkhodayan, Mark S. Young, B Crandall, R Tarrel, J Fease, J Scholz, Ruth E Anderson, T Hehr, Karen Gozel, R Shronts, D Tubman

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOcclusionModified Rankin ScaleRadiologyAngiographyMiddle cerebral arteryInternal carotid arteryStentCohortStroke (engine)Retrospective cohort studySurgeryComputed tomography angiographyIschemic strokeInternal medicineIschemia

Abstract

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Purpose To determine if the Alberta Stroke Program Early CT Score (ASPECTS) applied to non-contrast CT (NCCT) and CT angiography source images (CTA-SI) predicts clinical outcome in a cohort of patients with an acute middle cerebral artery (MCA) occlusion undergoing mechanical thrombectomy with stent-retrievers. Methods We conducted a retrospective review of patients who presented to our institution with an acute MCA occlusion and underwent mechanical thrombectomy with a stent-retriever from March 31st, 2012 until February 21st, 2013. Baseline clinical and procedural characteristics were recorded. Two experienced neurointerventionalists applied the ASPECTS to the pre-treatment NCCT and CTA-SI (if performed), with differences resolved by consensus. A “good scan” was defined as one with an ASPECTS ≥5. Clinical outcome at the time of hospital discharge or last clinical follow-up was determined utilising the modified Rankin Scale (mRS), with a good clinical outcome defined as an mRS of 0–2. Results Thirty patients presented to our institution with an acute MCA occlusion and underwent mechanical thrombectomy with a stent-retriever during the study period. Fifteen patients were female (50%) and 15 male (50%), with a mean age of 67.2 years (median 69 years, range 33–86 years). Mean admission NIHSS was 15.8 (median 16, range 5–27). Sixteen patients (53.3%) had received iv-tPA prior to endovascular treatment. Twenty-five patients (83.3%) had an MCA M1 segment occlusion and in 5 patients (16.7%) the occlusion extended to the internal carotid artery terminus. Mean time from NCCT to arterial puncture was 117 minutes (median 109 minutes, range 39–307 minutes). Mean time from CTA to arterial puncture was 104 minutes (median 92 minutes, range 16–272 minutes). Successful recanalisation (TICI 2b/3) was achieved in 26 patients (86.7%). Mean time from arterial puncture to successful recanalisation was 47 minutes (median 37 minutes, range 18–115 minutes). Mean time from symptom onset to successful recanalisation was 333 minutes (median 285 minutes, range 125–893 minutes). All pre-treatment NCCTs were categorised as “good scans”, with perfect inter-observer agreement. Twenty patients had a pre-treatment CTA performed (66.7%), 11 of which were categorised as “good scans” (55%), with substantial inter-observer agreement (kappa 0.8). Overall, a good clinical outcome was observed in 11 patients (36.7%), with a statistically-significant difference between the 12 patients age ≤65 years (66.7%) and the 18 patients age >65 years (16.7%, p-value 0.009). The table summarises the frequency of a good clinical outcome according to age group and pre-treatment CTA-SI ASPECTS. Conclusion In our cohort of patients with acute MCA occlusion undergoing mechanical thrombectomy with stent-retrievers, a “good scan” NCCT (ASPECTS ≥5) predicted a 67% likelihood of a good clinical outcome in patients age ≤65 years, while a “good scan” CTA (ASPECTS ≥5) predicted a 43% likelihood of a good clinical outcome in patients age >65 years. Clinical Outcome after Mechanical Thrombectomy with Stent-Retrievers by Age and CTA-SI ASPECTS. Abstract E-064 Table 1 All patients: ≤65 years: >65 years: p-value: CTA-SI ASPECTS: 0-4 5-10 0-4 5-10 0-4 5-10 N: 9 11 5 4 4 7 mRS 0–2: 1 7 1 4 0 3 mRS 3–6: 8 4 4 0 4 4 % mRS 0–2: 11.1 63.6 20 100 0 42.9 0.023 Disclosures J. Delgado Almandoz: 2; C; Covidien/ev3. Y. Kadkhodayan: None. M. Young: None. B. Crandall: 2; C; Covidien/ev3. R. Tarrel: None. J. Fease: None. J. Scholz: None. R. Anderson: None. T. Hehr: None. K. Gozel: None. R. Shronts: None. D. Tubman: 2; C; Covidien/ev3.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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