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Stroke: Working toward a Prioritized World Agenda

2010· editorial· en· W2140952786 on OpenAlexaff
Vladimir Hachinski, Geoffrey A. Donnan, Philip B. Gorelick, Werner Hacke, Steven C. Cramer, Markku Kaste, Marc Fisher, Michael Brainin, Alastair M. Buchan, Eng H. Lo, Brett E. Skolnick, Karen L. Furie, Graeme J. Hankey, Miia Kivipelto, John C. Morris, Peter M. Rothwell, Ralph L. Sacco, Sidney C. Smith, Yulun Wang, Alan Bryer, Gary A. Ford, Costantino Iadecola, Sheila Cristina Ouriques Martins, Jeffrey L. Saver, Veronika Skvortsova, Mark Bayley, Martin M. Bednar, Pamela W. Duncan, Lori Enney, Seth P. Finklestein, Theresa A. Jones, Lalit Kalra, Jeff Kleim, Ralph Nitkin, Robert Teasell, Cornelius Weiller, Bhupat Desai, Mark P. Goldberg, Wolf-Dieter Heiss, Osmo Saarelma, Lee H. Schwamm, Yukito Shinohara, Bhargava Trivedi, Nils Wahlgren, Ka Sing Wong, Antoine M. Hakim, Bo Norrving, Stephen Prudhomme, Natan M. Bornstein, Stephen M. Davis, Larry B. Goldstein, Didier Leys, Jaakko Tuomilehto

Bibliographic record

VenueInternational Journal of Stroke · 2010
Typeeditorial
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of OttawaUniversity Health NetworkWestern University
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMedicineStroke (engine)Clinical neurologyNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: The aim of the Synergium was to devise and prioritize new ways of accelerating progress in reducing the risks, effects, and consequences of stroke. METHODS: Preliminary work was performed by seven working groups of stroke leaders followed by a synergium (a forum for working synergistically together) with approximately 100 additional participants. The resulting draft document had further input from contributors outside the synergium. RESULTS: Recommendations of the Synergium are: Basic Science, Drug Development and Technology: There is a need to develop: (1) New systems of working together to break down the prevalent 'silo' mentality; (2) New models of vertically integrated basic, clinical, and epidemiological disciplines; and (3) Efficient methods of identifying other relevant areas of science. Stroke Prevention: (1) Establish a global chronic disease prevention initiative with stroke as a major focus. (2) Recognize not only abrupt clinical stroke, but subtle subclinical stroke, the commonest type of cerebrovascular disease, leading to impairments of executive function. (3) Develop, implement and evaluate a population approach for stroke prevention. (4) Develop public health communication strategies using traditional and novel (eg, social media/marketing) techniques. Acute Stroke Management: Continue the establishment of stroke centers, stroke units, regional systems of emergency stroke care and telestroke networks. Brain Recovery and Rehabilitation: (1) Translate best neuroscience, including animal and human studies, into poststroke recovery research and clinical care. (2) Standardize poststroke rehabilitation based on best evidence. (3) Develop consensus on, then implementation of, standardized clinical and surrogate assessments. (4) Carry out rigorous clinical research to advance stroke recovery. Into the 21st Century: Web, Technology and Communications: (1) Work toward global unrestricted access to stroke-related information. (2) Build centralized electronic archives and registries. Foster Cooperation Among Stakeholders (large stroke organizations, nongovernmental organizations, governments, patient organizations and industry) to enhance stroke care. Educate and energize professionals, patients, the public and policy makers by using a 'Brain Health' concept that enables promotion of preventive measures. CONCLUSIONS: To accelerate progress in stroke, we must reach beyond the current status scientifically, conceptually, and pragmatically. Advances can be made not only by doing, but ceasing to do. Significant savings in time, money, and effort could result from discontinuing practices driven by unsubstantiated opinion, unproven approaches, and financial gain. Systematic integration of knowledge into programs coupled with careful evaluation can speed the pace of progress.

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)0.051
metaresearch head score (Gemma)0.034
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.034
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0070.005
Science and technology studies0.0110.012
Scholarly communication0.0340.037
Open science0.0060.029
Research integrity0.0380.031
Insufficient payload (model declined to judge)0.0330.013

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 head0.021
GPT teacher head0.330
Teacher spread0.309 · 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
GenreEditorial

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

Citations152
Published2010
Admission routes1
Has abstractyes

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