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Analysis of Workflow and Time to Treatment on Thrombectomy Outcome in the Endovascular Treatment for Small Core and Proximal Occlusion Ischemic Stroke (ESCAPE) Randomized, Controlled Trial

2016· article· en· W2340148706 on OpenAlexafffund
Bijoy K. Menon, Tolulope T. Sajobi, Yukun Zhang, Jeremy Rempel, Ashfaq Shuaib, John Thornton, David Williams, Daniel Roy, Alexandre Y. Poppe, Tudor G. Jovin, Biggya L. Sapkota, Blaise Baxter, Timo Krings, Frank L. Silver, Donald Frei, Christopher Fanale, Donatella Tampieri, Jeanne Teitelbaum, Cheemun Lum, Dar Dowlatshahi, Muneer Eesa, Mark Lowerison, Noreen Kamal, Andrew M. Demchuk, Michael D. Hill, Mayank Goyal

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicineModified Rankin ScaleSolitaire Cryptographic AlgorithmGroinRandomized controlled trialStroke (engine)OcclusionEndovascular treatmentSurgeryConfidence intervalAnesthesiaIschemiaIschemic strokeInternal medicineAneurysm

Abstract

fetched live from OpenAlex

BACKGROUND: The Endovascular Treatment for Small Core and Proximal Occlusion Ischemic Stroke (ESCAPE) trial used innovative imaging and aggressive target time metrics to demonstrate the benefit of endovascular treatment in patients with acute ischemic stroke. We analyze the impact of time on clinical outcome and the effect of patient, hospital, and health system characteristics on workflow within the trial. METHODS AND RESULTS: Relationship between outcome (modified Rankin Scale) and interval times was modeled by using logistic regression. Association between time intervals (stroke onset to arrival in endovascular-capable hospital, to qualifying computed tomography, to groin puncture, and to reperfusion) and patient, hospital, and health system characteristics were modeled by using negative binomial regression. Every 30-minute increase in computed tomography-to-reperfusion time reduced the probability of achieving a functionally independent outcome (90-day modified Rankin Scale 0-2) by 8.3% (P=0.006). Symptom onset-to-imaging time was not associated with outcome (P>0.05). Onset-to-endovascular hospital arrival time was 42% (34 minutes) longer among patients receiving intravenous alteplase at the referring hospital (drip and ship) versus direct transfer (mothership). Computed tomography-to-groin puncture time was 15% (8 minutes) shorter among patients presenting during work hours versus off hours, 41% (24 minutes) shorter in drip-ship patients versus mothership, and 43% (22 minutes) longer when general anesthesia was administered. The use of a balloon guide catheter during endovascular procedures shortened puncture-to-reperfusion time by 21% (8 minutes). CONCLUSIONS: Imaging-to-reperfusion time is a significant predictor of outcome in the ESCAPE trial. Inefficiencies in triaging, off-hour presentation, intravenous alteplase administration, use of general anesthesia, and endovascular techniques offer major opportunities for improvement in workflow. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01778335.

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)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
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 head0.028
GPT teacher head0.283
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 designRandomized trial
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

Citations252
Published2016
Admission routes2
Has abstractyes

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