Persistence and Recurrence of Primary Hyperparathyroidism
Bibliographic record
Abstract
Persistent and recurrent primary hyperparathyroidism (PHPT) represent significant challenges in the management of PHPT. Persistent PHPT is defined as persistence of hypercalcemia following parathyroidectomy (PTX) or the recurrence of hypercalcemia within the first 6 months following surgery. Recurrent PHPT is defined as recurrence of hypercalcemia after 6 months following PTX and requires normalization of serum calcium prior to the recurrence. These conditions are often attributed to missed or ectopic glands, multiglandular disease, surgeon inexperience, or rare causes such as parathyromatosis and parathyroid carcinoma. Diagnosis requires a detailed biochemical evaluation, imaging studies, and exclusion of other causes of hypercalcemia as well as secondary causes of hyperparathyroidism. Preoperative imaging modalities, including neck ultrasound, SPECT-CT with 99m Tc-sestamibi scan, 4D-CT, 18F-Fluorocholine PET/CT, and PET/MRI are helpful in localizing abnormal parathyroid glands in cases requiring repeat surgery. Repeat surgery is associated with higher risk and requires an experienced surgeon. When surgery is not indicated or possible, medical management with cinacalcet and antiresorptive therapies may be considered. This review highlights the etiology, diagnostic approaches, and management strategies for persistent and recurrent PHPT, emphasizing the importance of multidisciplinary care in order to optimize outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".