Atypical post-renal transplantation hyperparathyroidism--further support for "removing all enlarged glands".
Bibliographic record
Abstract
Hyperparathyroidism is an important sequela of chronic renal failure, remains a considerable challenge to nephrologists, and can be seen as inevitable in patients undergoing long-term renal replacement therapy. As time with renal disease increases then so does the cumulative risk of hyperparathyroidism, and the eventual need for surgical parathyroidectomy when hyperparathyroidism becomes refractory to medical intervention. Parathyroidectomy before dialysis treatment has started, or after successful renal transplantation, is much less commonly performed than when the patient is receiving dialysis. Increasingly the propensity for residual parathyroid tissue left behind (by design or accident) at an initial parathyroidectomy to undergo progressive hyperplasia under the constant stimulus of uremia, and by so doing result in the need for a second, more complex, neck exploration, has increased support for initial total parathyroidectomy for patients on dialysis. The optimal operative procedure for autonomous hyperparathyroidism after successful renal engraftment is however less clearly established. We discuss two very unusual but instructive cases of post renal transplantation autonomous hyperparathyroidism requiring surgical parathyroidectomy. Using these cases as examples we discuss the various surgical options, and discuss the contentious issue of the place for autografting parathyroid tissue.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".