Tests of neuromuscular function are associated with fractures in patients with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Fractures are common in individuals with chronic kidney disease (CKD), and tests of neuromuscular function (NMT) discriminate well among fractured and non-fractured patients with Stage 5 CKD on dialysis. The ability of NMT to discriminate fracture status in patients with Stages 3-5 CKD is unknown. METHODS: In this cross-sectional study, we sought to determine in adult patients with Stages 3-5 CKD (eGFR by the Modification of Diet in Renal Disease equation) if NMT [timed up and go (TUG), 6-min walk (6MW) and grip strength] could discriminate fracture status (self-reported low-trauma fractures since age 40 and/or vertebral fractures by morphometry). We conducted logistic regression and receiver-operating characteristic (ROC) curves for each predictor [expressed as area under the ROC curves (AUROC) with 95% confidence intervals (CI)]. RESULTS: Data was available for 125 men and 86 women. The mean age was 63.3 ± 15.5 years, duration of CKD was 96.7 ± 125.3 months and one-third had diabetes. Patients with fractures were older and fell more frequently (P < 0.05). After adjusting for age, weight and sex, for every standard deviation increase in TUG and 6MW, the risk of fracture increased [odds ratio (OR): 1.68; 95% CI: 1.40-2.02] and decreased (OR: 0.53; 95% CI: 0.52-0.54), respectively. Both the TUG and 6MW could discriminate among those with and without fractures (AUROC: 0.90; 95% CI:0.84-0.95, AUROC: 0.87; 95% CI: 0.80-0.94, respectively). CONCLUSIONS: The TUG and 6MW are able to discriminate fracture status in patients with Stages 3-5 CKD. These tests do not require specialized expertise/equipment and are an inexpensive method to assess for the presence of fractures.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".