Hemiplegic Shoulder Pain Reduces Quality of Life After Acute Stroke
Bibliographic record
Abstract
OBJECTIVE: Hemiplegic shoulder pain is a common complication of stroke. The primary aim of this study was to determine the association of hemiplegic shoulder pain with health-related quality of life at 12 months after first stroke in a population-based registry. The secondary aim was to identify other factors associated with health-related quality-of-life outcomes. DESIGN: A prospective population-based study in a geographically defined region of Adelaide, South Australia was conducted. Multiple ascertainment methods identified all cases of stroke within a 12-month period. Objective and subjective measures were undertaken at baseline and at 4 and 12 months' follow-up. Multiple regression analyses identified independent variables (including exposure to shoulder pain and depression, 12-month dependence, access to formal rehabilitation) associated with health-related quality of life, defined by the summary index score derived from EuroQol-5D-3L at 12 months post-stroke. RESULTS: Hemiplegic shoulder pain, depression, increased dependency, stroke severity, and absence of initial rehabilitation were each associated with reduction in quality of life. Age, sex, stroke type, Oxfordshire classification, and discharge destination were not related to quality of life. CONCLUSION: Hemiplegic shoulder pain reduces health-related quality of life at 12 months. More effort should be directed towards screening and management of this frequent complication of stroke.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".