Challenges in managing extensive brown tumors and renal stones in a young man with parathyroid carcinoma and single kidney: Case report
Bibliographic record
Abstract
Parathyroid carcinoma is one of the rare causes of primary hyperparathyroidism, comprising less than 1 % of its cases. Diagnosing parathyroid carcinoma typically requires a comprehensive evaluation using clinical, histological, and radiological methods. Primary hyperparathyroidism secondary to parathyroid carcinoma often presents with hypercalcemia, bone abnormalities, and renal stones. Brown tumours, a rare and late manifestation of hyperparathyroidism, signify the final stage in bone remodelling, and they can be easily mistaken for bony metastases, which highlights the importance of distinguishing between them to ensure appropriate management and avoid unnecessary treatment. Due to the rarity of parathyroid carcinoma, there is currently no standardized staging system or specific guidelines for its management. Consequently, the involvement of a multidisciplinary team has become crucial in addressing this disease. Surgery is considered the primary treatment approach, while the role of adjuvant radiotherapy and chemotherapy remains controversial. With ongoing research, the treatment landscape for parathyroid carcinoma may evolve, offering new hope for improved outcomes and quality of life for affected individuals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".