Bibliographic record
Abstract
PURPOSE OF REVIEW: Fractures are common in patients with chronic kidney disease (CKD) and are associated with substantial morbidity and mortality. Bone mass measurements are commonly used to assess fracture risk in the general population, but the utility of these measurements in patients with CKD is unclear. RECENT FINDINGS: This review will outline the epidemiology and etiology of fractures in patients with CKD. Also, we will summarize the published data that describe the association between bone mass measurements and fracture in patients with CKD. SUMMARY: Patients with CKD suffer from fractures due to impairments in bone quantity, bone quality, and abnormalities of neuromuscular function. The complex etiology of fractures combined with the technical limitations of bone mineral density testing, both by dual energy X-ray absorptiometry and by peripheral quantitative computed tomography, limits the clinical utility of bone mass measurements for fracture prediction in CKD; this is particularly true among patients with stages 4 and 5 CKD. As such, clinicians should not routinely order bone mineral density testing in patients with CKD. Further research, to determine whether bone mineral density together with other noninvasive measures to assess bone strength can predict fracture, is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".