Intraoperative parathormone monitoring to predict operative success in patients with normohormonal hyperparathyroidism
Bibliographic record
Abstract
BACKGROUND: It is unclear whether parathyroidectomy guided by intraoperative parathormone (PTH) monitoring is predictive of operative success in patients with normohormonal hyperparathyroidism (nhHPT), a variant of primary hyperparathyroidism (pHPT) in which patients develop clinical manifestations similar to those of pHPT. This study examined intraoperative PTH monitoring in patients undergoing parathyroidectomy for nhHPT. METHODS: We performed a retrospective review of prospectively collected data from adult (age > 18 yr) patients who underwent parathyroidectomy for pHPT at 1 of 2 North American medical centres (in Calgary, Alberta, Canada, or Miami, Florida, United States) between 2007 and 2015. In patients with nhHPT, we used the criterion of an intraoperative decrease of more than 50% in PTH after abnormal gland excision. We defined operative success as continuous eucalcemia more than 6 months after parathyroidectomy. RESULTS: < 0.001); the corresponding values at 20 minutes were 35 (92.1%) and 286 (96.9%). Although 5 patients (13.2%) with nhHPT did not reach this criterion until 20 minutes, the rate of operative success was still 97.0% at long-term follow-up (mean 13 mo, range 6-67 mo). Of the 38 patients, 3 (7.9%) did not have an intraoperative decrease of more than 50% in PTH level by 20 minutes. Two of the 3 achieved operative success and remained normocalemic, and 1 developed recurrent disease at 12 months. CONCLUSION: Parathyroidectomy guided by intraoperative PTH monitoring accurately predicted operative success in patients with nhHPT. Intraoperative PTH monitoring may also help identify multiglandular disease in patients with nhHPT, using criteria similar to those in classic pHPT, with comparable operative success.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".