Incidence, Prevalence, and Risk Factors of Hemiplegic Shoulder Pain: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current systematic review aimed to investigate the incidence, prevalence, and risk factors causing hemiplegic shoulder pain (HSP) after stroke. Two independent authors screened titles and abstracts for the eligibility of the included studies in the electronic databases PubMed and Web of Science. Studies which reported the incidence, prevalence, and risk factors of HSP following stroke were included. The included studies were assessed using the Newcastle-Ottawa Scale for evaluating the quality of nonrandomized studies in meta-analyses. Eighteen studies were included in the final synthesis. In all studies, the number of patients ranged between 58 and 608, with the mean age ranging from 58.7 to 76 years. Seven included studies were rated as "good "quality, while one study rated "fair" and 10 studies rated "poor" quality. Eight studies reported incidence rate while 11 studies reported the prevalence of HSP following a stroke. The incidence of HSP was ranging from 10 to 22% in the metanalysis of the included studies. The prevalence of HSP was ranging from 22 to 47% in the metanalysis of the included studies. The most significant predictors of HSP were age, female gender, increased tone, sensory impairment, left-sided hemiparesis, hemorrhagic stroke, hemispatial neglect, positive past medical history, and poor National Institutes of Health Stroke Scale score. The incidence and prevalence of HSP after stroke vary considerably due to various factors. Knowledge of predictors is important to minimize the risk of developing HSP following a stroke.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it