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P-031 Mechanical thrombectomy with the solitaire device: is there a learning curve toward achieving rapid recanalization times?: Abstract P-031 Figure 1

2012· article· en· W2021718025 on OpenAlexaff
Muneer Eesa, Mohammed Almekhlafi, BK Menon, John W. P. Wong, Andrew M. Demchuk, Madhav Goyal

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSolitaire Cryptographic AlgorithmAngiographyStroke (engine)TIMIRevascularizationRadiologyComputed tomography angiographySurgeryNuclear medicineCardiologyIschemic strokeIschemiaMyocardial infarctionThrombolysis

Abstract

fetched live from OpenAlex

Introduction/Purpose We evaluated recanalization times with the Solitaire device in patients undergoing endovascular acute ischemic stroke therapy at our institution. Materials and Methods We reviewed patients who presented to our stroke center and in whom a Solitaire device was used for revascularization. Demographic data and stroke severity were obtained from chart review. Time points for CT scanning, angiography arrival, puncture, time of first deployment of the device and recanalization times were recorded from time-stamped PACS images and angiography records. Time intervals were calculated (CT to angiography arrival, angiography arrival to puncture, puncture to first deployment and deployment to recanalization). To evaluate time interval trends, recanalized patients were sequentially divided into three sequential groups. Overall CT to recanalization time and interval times between groups were compared using an analysis-of-variance (ANOVA) test. In addition, we also looked for difference between groups using the Scheffe's test correcting for multiple comparisons. All tests are two sided with a p value <0.05 considered to be statistically significant. Analysis was performed using Stata® V.12. Results 83 patients (38 female; mean age: 65.7±14.3) were treated with the Solitaire device from May 2009 to February 2012. The median NIHSS was 17. Recanalization (TIMI 2/3) occurred in 75 (90.4%) patients. CT to recanalization time showed a statistically significant decrease over time (p<0.01). This difference was maximal between first 25 and most recent 25 cases (161 to 94 min, p<0.01). The maximal contribution to this was from improvements in first deployment to recanalization time between the first 25 and second 25 patients (p=0.01) and between the first and third 25 patients (p=0.001) with modest contributions from moving patients from CT to the angiography-suite faster (p=0.02 between 1st and 3rd groups) and from puncture to first deployment (p=0.02 between 1st and 3rd groups). There was no statistically significant difference in time from angiography-suite arrival to puncture between the groups (Abstract P-031 figure 1). Abstract P-031 Figure 1 Conclusion There appears to be a learning curve involved in the efficient use of the Solitaire device in endovascular acute stroke therapy. Along with slight improvements in moving patients to angiography sooner and improved efficiency with intracranial access, mastering this device contributed significantly toward the overall drive to reduce recanalization times in stroke patients treated by an endovascular approach at our institution. This needs to be validated in a prospective manner to understand components of this learning curve that is potentially useful to educate new users to achieve faster recanalization times. Competing interests None.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.044
GPT teacher head0.297
Teacher spread0.253 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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