Normocalcemic parathyroid carcinoma: an unusual clinical presentation
Bibliographic record
Abstract
BACKGROUND: Parathyroid carcinoma is a rare cause of primary hyperparathyroidism and may be associated with significant disease related morbidity and mortality. Preoperative diagnosis remains a challenge, which may jeopardize appropriate and successful patient treatment. CASE PRESENTATION: We report a case of parathyroid carcinoma diagnosed in a 60-year-old woman that presented with a tender nodule located at the left lower thyroid pole and had been present for several years. Ultrasound examination revealed a 2.7 x 1.6 x 2.7 cm mass within the lower left lobe of the thyroid with cystic and solid areas. Lab measurement of the intact PTH level revealed it to be three times the upper limit of normal and the serum calcium level was within normal limits. A left thyroid lobectomy and isthmusectomy was carried out. Histopathological evaluation was diagnostic for a parathyroid carcinoma. At greater than two years of follow-up, the patient has had no evidence of disease recurrence and her serum PTH and calcium levels have remained within normal. CONCLUSION: Parathyroid carcinoma is a rare endocrine tumor which must be considered in the differential diagnosis of a nodular thyroid mass. En bloc resection remains the treatment of choice for this malignancy. Disease prognosis is influenced by the extent of the initial resection, the presence of metastases, and adequate long-term follow-up.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".