Tibial Bone Geometry in Chronic Stroke Patients: Influence of Sex, Cardiovascular Health, and Muscle Mass
Bibliographic record
Abstract
This study aimed to examine the geometry of the tibia in chronic stroke survivors. Fifty-five ambulatory individuals with chronic stroke were included in the study. pQCT was used to obtain a cross-sectional scan of the tibia at the 30% site on both the paretic and nonparetic sides. Leg lean mass was derived from a total body scan using DXA. Each subject was also evaluated for peak oxygen consumption rate, spasticity, and functional mobility. Paired t-tests were used to compare the pQCT parameters between the two sides. Multiple linear regression analysis was used to identify the significant determinants of tibial bone strength index (BSI). In men, marrow cavity area on the paretic side was significantly greater than the nonparetic side (p = 0.011), whereas the total bone area showed no significant side-to-side difference (p = 0.252). In women, total bone area on the paretic side was significantly smaller than the nonparetic side (p = 0.003), whereas the marrow cavity area had no side-to-side difference (p = 0.367). Peak oxygen consumption (r(2) = 0.739, F(5,49) = 22.693, p < 0.001) and paretic leg lean mass (r(2) = 0.802, F(6,48) = 32.475, p < 0.001) remained independently associated with tibial BSI, after controlling for age, sex, body mass index, years since stroke onset, and physical activity level. The geometry of the tibia in stroke patients showed sex-specific side-to-side differences. The results suggested that, whereas endosteal resorption was apparent in men, periosteal resorption was more predominant in women. The results also highlight the potential importance of promoting cardiovascular health and leg muscle mass in enhancing bone geometry in chronic stroke survivors.
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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) | 0.002 | 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".