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Record W2048311900 · doi:10.1586/14737175.2014.968130

The next revolution in stroke care

2014· review· en· W2048311900 on OpenAlexaff
Robert Teasell, Danielle B. Rice, Marina Richardson, Nerissa Campbell, Mona Madady, Norhayati Hussein, Manuel Murie-Fernández, Stephen J. Page

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2014
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteWestern UniversityParkwood Institute
Fundersnot available
KeywordsStroke (engine)RehabilitationMedicinePopulationQuality of life (healthcare)Cause of deathAcute strokePhysical therapyNursingEmergency departmentDiseaseEnvironmental health

Abstract

fetched live from OpenAlex

Stroke is the second leading cause of death and disability worldwide. Initiatives to decrease the burden of stroke have largely focused on prevention and acute care strategies. Despite considerable resources and attention, the focus on prevention and acute care has not been successful in changing the clinical trajectory for the majority of stroke patients. While efforts to prevent strokes will continue to have an impact, the total burden of stroke will increase due to the aging population and decreased mortality rates. There is strong evidence for the effectiveness of rehabilitation in better managing stroke and its related disabilities. The time has come to shift the attention in stroke care and research from prevention and cure to a greater focus and investment in the rehabilitation and quality of life of stroke survivors. The rebalancing of stroke care and research initiatives requires a reinvestment in rehabilitation and community reintegration of stroke survivors.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.003

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 head0.065
GPT teacher head0.391
Teacher spread0.326 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations35
Published2014
Admission routes1
Has abstractyes

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