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Record W2097892227 · doi:10.1111/ijs.12614

Neurothrombectomy Trial Results: Stroke Systems, Not Just Devices, Make the Difference

2015· article· en· W2097892227 on OpenAlexaff
J Mocco, Kyle M Fargen, Mayank Goyal, Elad I. Levy, Peter Mitchell, Bruce Campbell, Charles B.L.M. Majoie, Diederik W.J. Dippel, Pooja Khatri, Michael D. Hill, Jeffrey L. Saver

Bibliographic record

VenueInternational Journal of Stroke · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)CertificationClinical trialRandomized controlled trialQuality (philosophy)Quality managementIntensive care medicinePhysical therapyOperations managementSurgeryManagement systemInternal medicineManagement

Abstract

fetched live from OpenAlex

The overwhelming benefit demonstrated in the four recent randomized trials comparing intra-arterial therapies to medical management alone will have a transformative effect on the emergent management of strokes throughout the world. New generation neurothrombectomy devices were critical to trial success, but not the sole driver of patient outcomes in these trials. Patients in the positive trials were treated at hospitals with complex, efficient, resource-rich, team-based stroke systems in place. To ensure attainment of trial results in actual practice, patients should receive treatment at facilities certified as having the resources, personnel, organization, and continuous quality improvement processes characteristic of trial centers. It is our hope that, through greater education initiatives, robust resource investment, and developing quality-based certification processes, the results demonstrated by these trials may be extrapolated to greater numbers of centers - in turn allowing greater access for patients to high-quality, advanced stroke care.

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)0.041
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.097
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.068
GPT teacher head0.327
Teacher spread0.259 · 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 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".

Quick stats

Citations31
Published2015
Admission routes1
Has abstractyes

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