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Record W2052729137 · doi:10.1097/mrr.0b013e3282fc0f33

Health-related quality of life after stroke: what are we measuring?

2008· review· en· W2052729137 on OpenAlexafffund
Katherine Salter, Matthew B. Moses, Norine Foley, Robert Teasell

Bibliographic record

VenueInternational Journal of Rehabilitation Research · 2008
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsParkwood InstituteWestern UniversityLawson Health Research Institute
FundersHeart and Stroke Foundation of Canada
KeywordsOperationalizationQuality of life (healthcare)Construct (python library)PsychologyScale (ratio)Stroke (engine)Well-beingCognitionActivities of daily livingClinical psychologyApplied psychologyGerontologyMedicinePsychiatryPsychotherapistComputer science

Abstract

fetched live from OpenAlex

As there is no single, accepted definition of health-related quality of life (HRQOL), it is assumed to be a broad, multidimensional construct referring to those aspects of people's lives that reasonably relate to their health. Although many scales are used to assess HRQOL, the operationalization of this construct within each tool is unclear. To clarify what each tool is measuring, this study reviewed eight scales commonly used to evaluate HRQOL after stroke. Two reviewers classified scale items from five generic and three stroke-specific scales within an established framework with nine dimensions; physical functioning, symptoms, global judgments of health, psychological well-being, social well-being, cognitive functioning, role activities, personal constructs, and satisfaction with care. All scales reviewed provide multidimensional assessment, but vary in number and combination of dimensions. All include assessment of physical functioning and most incorporate concepts, such as psychological well-being, social well-being, and role activities. One generic (Sickness Impact Profile) and two stroke-specific scales (Stroke Impact Scale and Stroke-Specific Quality of Life Scale) seemed most comprehensive. Evaluated against a common framework of dimensions, scales commonly used in the assessment of HRQOL after stroke provide varying multidimensional assessments of aspects of life function related to health. Whether any of these assessments are sufficient to describe HRQOL in its entirety is unclear.

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)0.006
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.216
GPT teacher head0.495
Teacher spread0.279 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations108
Published2008
Admission routes2
Has abstractyes

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