Reducing the Risk of Re‐Fracture in the Dialysis Population: Is it Time to Consider Therapy with PTH Analogues?
Bibliographic record
Abstract
Dialysis patients are at high risk for fracture, with published rates in excess of a 20% probability of fracture over the next 10 years of dialysis. Unfortunately, there is no accepted methodology for quantifying this risk in advance of the first fracture; conventional bone densitometry performs unreliably in this role, in contrast to its utility in elderly patients with osteoporosis. The KDIGO clinical guidelines emphasize the importance of bone turnover in the development of renal osteodystrophy with high bone turnover strongly associated with uncontrolled secondary hyperparathyroidism, and adynamic bone disease (ABD) defined as a very low bone turnover state associated with functional hypoparathyroidism. It is likely that fractures occur in association with both extremes of uremic bone turnover, in addition to the known risk factors for developing osteoporosis prior to an individual developing end-stage renal failure. No systematic evidence has been forthcoming on therapy to reduce the risk of re-fracture after a dialysis patient presents with a first fracture. Anti-resorptive therapy might be effective in high turnover uremic bone disease and has been demonstrably effective in reducing fracture risk in osteoporotic patients, but there is only post hoc evidence that cinacalcet might reduce the incidence of fractures, and almost no evidence on outcomes from the use of bisphosphonates in dialysis patients. Fractures associated with ABD present a particular challenge. Although aluminum intoxication has been an important cause of skeletal fracturing in the past, this is a rare event today, when nonaluminum containing dietary phosphate binders are routinely prescribed. We suggest that the use of an anabolic agent would be a more plausible approach to the management of ABD (rather than anti-resorptive agents) and propose that a "proof-of-concept" trial with a PTH analogue such as teriparatide should be considered for these patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".