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Record W2248348530 · doi:10.1161/str.43.suppl_1.a21

Abstract 21: Patient and Hospital Characteristics Associated with Assessment for Rehabilitation During Hospitalization for Acute Ischemic Stroke

2012· article· en· W2248348530 on OpenAlexaff
Janet Prvu Bettger, Lisa A. Kaltenbach, Mathew J. Reeves, Eric E. Smith, Gregg C. Fonarow, Lee H. Schwamm, Eric D. Peterson

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)RehabilitationLogistic regressionEmergency medicinePhysical therapyOdds ratioInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Delays in post-stroke rehabilitation can negatively affect functional recovery and health-care costs. While clinical guidelines recommend that all stroke patients receive a standardized assessment during the acute hospitalization, the degree and determinants of acute assessment for rehabilitation (AAR) following ischemic stroke are unknown. Methods: We analyzed data from 1540 Get With The Guidelines-Stroke hospitals from 01/08/2008 to 03/31/2011. Patients who died in hospital, left AMA, or were transferred in from or out to another acute hospital were excluded. Univariate (chi-square or Wilcoxon as appropriate) and multivariable logistic regression analyses with GEE were used to identify factors independently associated with an AAR while accounting for within hospital clustering. Results: Among 616,982 ischemic stroke patients, 89.5% had an AAR documented. Those without AAR were more likely white, older, female, unable to ambulate prior to admission, from a chronic care facility, have Medicare health insurance and comorbid conditions. Also without an AAR were those with moderate-severe stroke (NIHSS≥6), unable to ambulate on day 2, and were not cared for in a stroke unit. Nine percent of patients discharged home without services were not assessed for rehabilitation. In multivariable analysis, many factors were independently associated with receiving an AAR; however, patients with the greatest odds (OR>1.2) were of Black race, without a history of carotid stenosis, ambulating independently prior to admission, had stroke symptoms outside of a healthcare facility, were treated at a Northeast hospital, in a stroke unit, had complications from thrombolytic therapy, and were ambulating on hospital day 2 ( Table ). Conclusion: Although 90% of ischemic stroke patients received an AAR, the results suggest important subpopulations were overlooked. Quality improvement efforts are needed to ensure that all stroke patients are assessed and referred for the appropriate level of rehabilitation care for their needs. Further research of the unexplained variation in AAR is warranted.

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)0.006
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.006
GPT teacher head0.267
Teacher spread0.261 · 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
Published2012
Admission routes1
Has abstractyes

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