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Record W4289201607 · doi:10.1017/cjn.2022.280

Barriers to Care for Poststroke Visual Deficits in Alberta, Canada

2022· article· en· W4289201607 on OpenAlexafffundvenueabout
Kiran Pohar Manhas, Karim F. Damji, Katelyn Brehon, Jennis Jiang, Peter Faris, Fiona Costello

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsAlberta HealthUniversity of CalgaryUniversity of AlbertaAlberta Health Services
FundersAlberta Health Services
KeywordsStroke (engine)RehabilitationReferralMedicineObservational studyQuality of life (healthcare)Acute strokePhysical therapyFamily medicineNursingEmergency departmentInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT: Poststroke visual impairment (VI) negatively affects rehabilitation potential and quality of life for stroke survivors. In this cross-sectional observational study, stroke survivors and providers were surveyed to quantify perspectives regarding care for poststroke VI in Alberta, Canada (n = 46 survivors; n = 87 providers). Few patients (35%) felt prepared to cope with VI at the time of discharge from acute stroke and inpatient rehabilitation settings. Less than 25% of stroke survivors, and <16% of providers, felt referral processes were adequate. 95.2% of providers and 82% of stroke survivors advocated for a provincial clinical pathway to improve care quality for poststroke VI.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.301

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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

Citations0
Published2022
Admission routes4
Has abstractyes

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