Clinical Characteristics and Bone Mineral Density Score in Post-Stroke Neuromuscular Deficit
Bibliographic record
Abstract
Background: Disuse osteoporosis in hemiparetic patients often results in significant morbidity, decreased quality of life, and different clinical characteristics. The study aimed to investigate the effect of these clinical factors on bone mineral density (BMD). Methods: This was an analytical observational study with a cross-sectional method evaluating hemiparetic patients at Cipto Mangunkusumo Hospital from 2018 to 2019. BMD (g/cm2) was assessed using dual energy X-ray absorptiometry (DXA) on the spine and both sides of the body. The relationship and correlation between BMD and delta BMD scores with clinical characteristics were analyzed. A linear regression test was used to assess the correlation between variables. Results: A total of 34 participants were recruited for this study. There was a difference between the healthy and paretic side of BMD of both hip and wrist (P < 0.001), strong positive correlation between the onset of hemiparesis and wrist and hip delta BMD (r = 0.779, P = 0.001 and r = 0.791, P = 0.001), and significant association between delta BMD and age and motor strength. Multivariate analysis shows that the onset of hemiparesis was a strong predictor of delta BMD (aR2 wrist = 0.486, aR2 hip = 0.614). There was a 7.36% decrease in the mean BMD score of the paretic side compared to the non-paretic side. Conclusion: A low BMD score is prevalent in seven out of 10 patients with post-stroke neuromuscular deficit. Age, limb strength, the onset of hemiparesis, and rehabilitation compliance are associated with decreased BMD among patients with post-stroke neuromuscular deficit.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".