Brown tumors in dialyzed patients with secondary hyperparathyroidism: Report of 16 cases
Bibliographic record
Abstract
Brown tumors (BTs) are relatively uncommon but they are serious complications of renal osteodystrophy. The objective of this study was to analyze the clinical, biological, and radiological characteristics of 16 patients with BTs provoked by secondary hyperparathyroidism (sHPT) and its response to the decrease in parathyroid hormone levels after parathyroidectomy (PTX). The management of that uncommon condition was also reviewed. We conducted a retrospective study including 16 end-stage renal disease patients who underwent subtotal PTX between 1997 and 2007 for severe sHPT with BTs. Our study included 10 men and 6 women, whose average age was 34 years. All patients were on dialysis. Ten of them were on dialysis for more than 5 years. The median duration on dialysis was 84 months. Patients included suffered from swellings associated with functional limitations. BTs had multiple locations in 7 patients. Jaw was the most frequent location (62%). Radiography and tomodensitometry demonstrated a mixed radio lucent and radio-opaque lesions with an expansion of the cortical bone. Bone scan demonstrated an increased uptake of lesions. Chirurgical treatment was indicated in all cases because of severe refractory sHPT with functional limitations and/or disfiguring deformities. In all cases, BTs stopped its progression and even decreased in size. However, it was insufficient in four cases, which required a surgical resection. PTX remains an efficacious approach in resistant cases of sHPT with persistent BTs.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".