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Record W4387330631 · doi:10.1136/svn-2022-002146

Stroke depression: a concept with clinical applicability

2023· review· en· W4387330631 on OpenAlexaff
Jun Mu, Arun Ravindran, Pim Cuijpers, Yiqing Shen, Wen‐Song Yang, Qi Li, Xinyu Zhou, Peng Xie

Bibliographic record

VenueStroke and Vascular Neurology · 2023
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersChinese Academy of Medical Sciences
KeywordsStroke (engine)Depression (economics)MedicineIntervention (counseling)Incidence (geometry)Intensive care medicinePsychiatry

Abstract

fetched live from OpenAlex

Stroke is a common neurological condition and among the leading causes of death and disability worldwide. Depression is both a risk factor for and complication of stroke, and the two conditions may have a complex reciprocal relationship over time. However, the secondary effects of depression on stroke are often overlooked, resulting in increased morbidity and mortality. In the previous concept of 'poststroke depression', stroke and depression were considered as two independent diseases. It often delays the diagnosis and treatment of patients. The concept 'stroke depression' proposed in this article will emphasise more the necessity of aggressive treatment of depression in the overall management of stroke, thus to reduce the incidence of stroke and in the meantime, improve the prognosis of stroke. Hopefully, it will lead us into a new era of acute stroke intervention.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.062
GPT teacher head0.387
Teacher spread0.325 · 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.

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

Citations11
Published2023
Admission routes1
Has abstractyes

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