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Poststroke “Pushing”

2004· article· en· W2097631515 on OpenAlexafffundabout
Cynthia J. Danells, Sandra E. Black, David J. Gladstone, William E. McIlroy

Bibliographic record

VenueStroke · 2004
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsHeart and Stroke FoundationUniversity of Toronto
FundersMcMaster UniversityHeart and Stroke Foundation of Canada
KeywordsMedicineHemiparesisStroke (engine)RehabilitationBalance (ability)Physical therapyPhysical medicine and rehabilitationProspective cohort studyStroke recoveryFunctional Independence MeasureSurgeryLesion

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Patients with hemiparetic stroke have impaired balance control. Some patients ("pushers") are resistant to accepting weight on and actively "push" away from the nonparetic side. This research identified pushers from stroke patients with moderate to severe hemiparesis and examined longitudinal changes in symptoms, level of impairment, and functional independence. METHODS: Prospective sample of hemiparetic stroke patients (n=65) located in Toronto, Canada. Detailed clinical assessments were performed within 10 days postonset, at 6 weeks, and at 3 months. RESULTS: At 1 week after stroke, 63% of patients demonstrated features of pushing. In 62% of pushers, symptoms resolved by 6 weeks, whereas in 21%, pushing symptoms persisted at 3 months. Motor recovery and functional abilities at 3 months were significantly lower among the pushers compared with the nonpushers. Pushers also had a significantly longer hospital length of stay (89 days versus 57 days). It is noteworthy that motor and functional recovery improved significantly over the 3-month study period for both pushers and nonpushers. CONCLUSIONS: Identification of stroke patients with pushing symptoms has prognostic implications for recovery. In light of this potential recovery, rehabilitation specialists need to refine treatment approaches for the pushers to further improve functional outcome.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.521

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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations110
Published2004
Admission routes3
Has abstractyes

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