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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".