Multiple Brown Tumors Caused by Primary Hyperparathyroidism as a Differential Diagnosis to Multiple Osteolytic Bone Metastases: A Case Report
Bibliographic record
Abstract
Brown tumors are benign osteolytic lesions, which usually respect the bone cortex. Since these lesions may resemble bone metastases, it is important to consider them as a potential differential diagnosis. They occur as a result of increased parathyroid hormone (PTH) secretion mainly due to primary or secondary hyperparathyroidism. We present a case of multiple osteolytic lesions incidentally found on X-ray examinations in a patient, who had a radius fracture after a low-energy trauma. Due to the suspicion of multiple bone metastases, one of them mimicking sequels after a pathological fracture in the ulna, the patient had a positron emission tomography/computed tomography (PET/CT) and a magnetic resonance imaging (MRI) scan performed supporting the existence of pervasive bone lesions without suggesting a primary malignancy. The blood samples showed highly elevated ionized calcium and PTH levels. Therefore, an ultrasound examination and parathyroid scintigraphy were performed, revealing a hyperfunctioning parathyroid adenoma. After removal of the adenoma, the PTH level normalised and the bone changes regressed without surgical intervention. J Endocrinol Metab. 2020;10(3-4):94-100 doi: https://doi.org/10.14740/jem637
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".