Advances in the management of parathyroid carcinoma
Bibliographic record
Abstract
Parathyroid carcinoma (PCA) is a rare malignancy accounting for approximately 1% of all patients with primary hyperparathyroidism. It is characterised by excessive parathyroid hormone (PTH) production. This manuscript reviews recent advances in the management of parathyroid carcinoma, focusing on molecular insights, diagnostic modalities, surgical innovations, adjuvant therapies, and emerging targeted treatments. Recently published manuscripts (between 2022 and 2023) were obtained from Medical Literature Analysis and Retrieval System Online (Medline), Excerpta Medica (Embase), Cochrane Central Register of Controlled Trials (CENTRAL), and European Union Drug Regulating Authorities Clinical Trials (EudraCT). These were assessed for their relevance in terms of the diagnosis and management of patients with PCA. This manuscript explores the role of genetic profiling and presents case studies illustrating successful management strategies. The manuscript also discusses the ongoing challenges in the management of parathyroid carcinoma, suggesting future research directions and potential therapeutic avenues.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".