Osteoporotic fracture rates in chronic hemodialysis and effect of heparin exposure: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Patients receiving chronic hemodialysis treatments are at a higher risk of fracture compared to the general population. While the use of heparin during dialysis is crucial to avoid thrombosis of the extracorporeal circuit, the association of unfractionated heparin (UFH) and the risk of osteoporotic fracture has been shown for many years. However, this association was not as clear for low-molecular-weight heparin (LMWH) and the few collected data originated from studies among pregnant women. Our aim was to measure osteoporotic fracture rate among hemodialysis patients and to evaluate the association of LMWH compared to UFH in hemodialysis. METHODS: A retrospective cohort study was conducted on data extracted from the RAMQ and Med-Echo databases from January 2007 to March 2013 with patients chronically hemodialyzed in 21 participating centers. Incidence rates for each fracture sites were measured per 1000 patient-year (p-y) and their 95% confidence intervals (CI). Osteoporotic fracture risk for a first event with LMWH compared to UFH was estimated using a cox proportional hazard model using demographics, comorbidities and drug use as covariates. RESULTS: 4796 patients undergoing chronic hemodialysis were identified. The incidence rate for all fracture sites was 22.7 /1000 p-y (95% CI: 19.6-26.1) and 12.8 /1000 p-y (95% CI: 10.5-15.4) for hip and femur fractures. We found a similar risk of osteoporotic fracture for LMWH compared to UFH (adjusted HR = 1.01; 95%CI: 0.72-1.42). Age and malignancy increased the risk of fracture while cerebrovascular disease decreased the risk of fracture. CONCLUSIONS: Compared to UFH, LMWH did not change the risk of osteoporotic fracture when used for the extracorporeal circuit anticoagulation in chronic hemodialysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".