Low Serum Parathyroid Hormone Is a Predictor of Early Death after Hip Arthroplasty in Hemodialysis Patients
Bibliographic record
Abstract
A high mortality rate after hip arthroplasty has been reported in hemodialysis patients; however, there has been no previous study on predictors of mortality after hip arthroplasty so far. Objectives: The purpose of the present study was to identify any risk factors associated with early death in hemodialysis patients undergoing hip arthroplasty. Methods: We retrospectively reviewed 34 patients on hemodialysis who underwent hip arthroplasty between 1994 and 2001. The average age was 60 years, and the average hemodialysis duration was 116 months at the time of operation. Body mass index (BMI), operating time, and total blood loss were reviewed. Serum levels of albumin (Alb), calcium (Ca), phosphorus (P), alkaline phosphatase (Alp), and intact parathyroid hormone (PTH) were measured preoperatively. Results: Of these 34 patients, 9 died (26%). There were 2 perioperative deaths and 7 during follow‐up period between 2 and 19 months. No significant difference was found with respect to patient age, hemodialysis duration, Alb, Ca, P, Alp, operating time, and total blood loss. Patients with lower BMI and PTH levels had an earlier mortality than patients with higher BMI and PTH levels (p < 0.05 and p < 0.01). Conclusion: We conclude that despite an intensive care directed to our hemodialysis patients, the incidence of early death after hip arthroplasty is still high. If low BMI and serum levels of low PTH were detected before operation, we should pay special attention to early mortality after hip arthroplasty.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".