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Record W2796549183 · doi:10.1155/2018/1621830

Depressive Symptoms in Individuals after Stroke in a Home-Based Rehabilitation Setting

2018· article· en· W2796549183 on OpenAlexaff
Julianne Vermeer, Amanda McIntyre, Shannon Janzen, Danielle B. Rice, Laura Allen, David Ure, Robert Teasell

Bibliographic record

VenueNeurology Research International · 2018
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsWestern UniversitySt Joseph's Health CareParkwood InstituteLawson Health Research Institute
Fundersnot available
KeywordsMedicineLogistic regressionPopulationAlgorithmDepression (economics)RehabilitationStroke (engine)ReferralDepressive symptomsPhysical therapyInternal medicinePsychiatryFamily medicineMathematicsCognition

Abstract

fetched live from OpenAlex

Poststroke depression has been shown to affect rehabilitation progress. This study evaluated patients after stroke who actively participated in a home-based rehabilitation program to determine variables that correlated with depressive symptoms in this population. A retrospective review of patients who were provided rehabilitation by Community Stroke Rehabilitation Team clinicians between January 1, 2009, and September 30, 2015, was completed. Logistic regression analysis was conducted to determine which demographic and outcome variables (Functional Independence Measure [FIM™] and Reintegration to Normal Living Index [RNLI]) were independently associated with depressive symptoms, as defined by Patient Health Questionnaire (PHQ-9) scores. 889 patients (53.2% male, mean age = 69.8 years) were included. Based on PHQ-9 scores, 89.7% of patients presented with no or mild depressive symptoms (PHQ-9 &lt; 10) and 10.3% were considered to have moderate to severe depressive symptoms (PHQ-9 ≥ 10). The regression demonstrated that referral from outpatient, community care access centre, or community settings (OR = 1.89, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.04</mml:mn></mml:math>), low RNLI scores (OR = 0.92; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.001</mml:mn></mml:math>), and younger age (OR = 0.96; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn fontstyle="italic">0.001</mml:mn></mml:math>) predicted patients having moderate to severe depressive symptoms. Given the impact of poststroke depression on rehabilitation, clinicians should consider the potential impact of referral source, community reintegration, and age when monitoring and treating depressive symptoms.

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)0.003
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 score0.019
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.369
Teacher spread0.349 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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