Successful Deceased Donor Parathyroid Allotransplantation: A Novel Approach in a Patient with Severe Refractory Hypoparathyroidism After Thyroidectomy for Thyroid Cancer
Bibliographic record
Abstract
Background Hypoparathyroidism following thyroid surgery presents significant challenges, often leading to debilitating symptoms and reduced quality of life despite conventional treatment. We describe a patient who had a staged total thyroidectomy for low-risk thyroid cancer and developed severe refractory iatrogenic hypoparathyroidism in whom we performed the first successful fresh normal-tissue deceased donor parathyroid transplant in an immune-naïve recipient. Methods A rigorous protocol for donor selection and transplantation was developed. Donor criteria aimed at minimizing infectious and immunological risks. Surgical techniques involved retrieval and transplantation of healthy parathyroid glands from a deceased donor into the recipient's muscle tissue, followed by immunosuppression. Results Following transplantation, the patient exhibited rapid resolution of symptoms, normalization of calcium levels, and cessation of calcium supplementation. Follow-up-has revealed sustained graft function without the need for additional therapy. Conclusion Deceased donor parathyroid allotransplantation emerges as a promising therapeutic option for severe refractory hypoparathyroidism, underscoring the potential for physiologic cure and improved quality of life in patients with this debilitating complication of thyroid cancer surgery. Further research is warranted to validate the efficacy and safety of this innovative approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".