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Record W4293090079 · doi:10.4236/ojtr.2022.103010

Impact of Diabetes, Hypertension and Heart Failure on Stroke Rehabilitation Care

2022· article· en· W4293090079 on OpenAlexafffundabout
Eric F. Tanlaka, Jennifer Voth, Nathania Liem

Bibliographic record

VenueOpen Journal of Therapy and Rehabilitation · 2022
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsHôtel-Dieu Grace HealthcareUniversity of Windsor
FundersUniversity of Windsor
KeywordsMedicineRehabilitationStroke (engine)ComorbidityHeart failureDiabetes mellitusPhysical therapyType 2 diabetesInternal medicineRetrospective cohort studyEmergency medicine

Abstract

fetched live from OpenAlex

Background: Stroke patients who have multiple comorbidities at inpatient rehabilitation admission might experience poorer outcomes than those without comorbidities. Some differences in outcomes between these two groups may be based on age and type of comorbidity. Materials and Methods: Retrospective administrative data from an inpatient stroke rehabilitation unit in a Southwestern Ontario hospital were examined to determine the independent associations between diabetes, hypertension, and heart failure in stroke patients and rehabilitation length of stay (LOS), functional gains in rehabilitation, and discharge destination. We also examined the associations between CHADS2 score and rehabilitation LOS, functional gains in rehabilitation, and discharge destination. Results: Seven hundred and seven cases of stroke subcategorized as experiencing mild (n = 193), moderate (n = 454), and severe (n = 60) stroke were included in the study. Of these patients, 16.4% (n = 116) had type 2 diabetes, 58.7% (n = 415) had hypertension, and 5.8% (n = 41) had congestive heart failure (CHF) prior to stroke. CHF patients were significantly (p = 0.02) older, had significantly (p = 0.014) lower mean FIM gains and were discharged to residential care facilities compared to non-CHF cases. A higher CHADS2 score was significantly associated with Lower FIM gains and discharge to longer term settings. Conclusion: Significant differences exist in the functional gains and discharge disposition of stroke patients based on age of patient, type of comorbidity in stroke, and CHADS2 score.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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