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Stroke and Vascular Cognitive Impairment

2007· article· en· W2090652960 on OpenAlexaff
Vladimir Hachinski

Bibliographic record

VenueStroke · 2007
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineDiseaseDementiaStroke (engine)CognitionClinical trialPaceSet (abstract data type)RigourNeuroscienceIntensive care medicinePsychologyPsychiatryPathology

Abstract

fetched live from OpenAlex

Advances in stroke are occurring at an unprecedented pace, but often in disciplinary isolation and without optimal mechanisms for systematically translating, integrating and applying the findings. Knowledge accrues in pieces, but is understood in patterns. To optimize knowledge acquisition and application, infrastructures and systems need to be set up along with appealing incentives. The approach needs to be transdisciplinary, going beyond the bounds of any given discipline, reciprocally translational, and transactional, meaning that the interchanges have to yield previously agreed benefits to the parties (The Triple T Approach). A new breed of leaders needs to be developed and nurtured to catalyze the process. Opportunities abound. Stroke and most brain diseases share the same pathophysiological fundamental mechanisms. An integrated, systematic approach to these processes could yield not only greater understanding but new, common therapeutic targets for several diseases. Biphasic clinical trials could combine the best features of pragmatic and explanatory, randomized clinical trials. The greatest opportunity of all may be the largely under-explored and under-exploited borderlands between cerebrovascular and Alzheimer disease. One in three of us will have a stroke, become demented, or both. For each person who has a stroke or Alzheimer disease, two have some cognitive impairment short of dementia, often subclinical cerebrovascular disease on a substrate of Alzheimer changes. The fact that cerebrovascular and Alzheimer disease share the same risk factors, provide a great opportunity for prevention, if implemented at the "brain at risk" stage. Systematically integrating what we know and evaluating what we do could spur progress. Research is not only an activity but an attitude. Making evaluation and incentives to excel part of the funding of all stroke activities would yield far ranging cumulative improvements in all aspects of stroke. No system can replace the individual initiative, creativity and insights that lead to the great discoveries, but progress is not made by breakthroughs alone. No one's work is so exalted that it cannot be improved, nor so humble that it has no value. We can all make a difference.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.251
Teacher spread0.244 · 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.

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

Citations105
Published2007
Admission routes1
Has abstractyes

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