Recurrence After Surgery for Primary Hyperparathyroidism in 517 Patients With Multiple Endocrine Neoplasia Type 1
Bibliographic record
Abstract
OBJECTIVE: To assess recurrence according to the type of surgery for primary hyperparathyroidism (pHPT) in multiple endocrine neoplasia type 1 ( MEN1 ) patients and to identify the risk factors for recurrence after the initial surgery. BACKGROUND: In MEN1 patients, pHPT is multiglandular, and the optimal extent of initial parathyroid resection influences the risk of recurrence. METHODS: MEN1 patients who underwent initial surgery for pHPT between 1990 and 2019 were included. Persistence and recurrence rates after less than subtotal parathyroidectomy (LTSP) and subtotal parathyroidectomy (STP) were analyzed. Patients with total parathyroidectomy with reimplantation were excluded. RESULTS: Five hundred seventeen patients underwent their first surgery for pHPT: 178 had LTSP (34.4%) and 339 STP (65.6%). The recurrence rate was significantly higher after LTSP (68.5%) than STP (45%) ( P < 0.001). The median time to recurrence after pHPT surgery was significantly shorter after LTSP than after STP: 4.25 (1.2-7.1) versus 7.2 (3.9-10.1) years ( P < 0.001). A mutation in exon 10 was an independent risk factor of recurrence after STP (odds ratio = 2.19; 95% CI: 1.31; 3.69; P = 0.003). The 5 and 10-year recurrent pHPT probabilities were significantly higher in patients after LTSP with a mutation in exon 10 (37% and 79% vs 30% and 61%; P = 0.016). CONCLUSIONS: Persistence, recurrence of pHPT, and reoperation rate are significantly lower after STP than LTSP in MEN1 patients. Genotype seems to be associated with the recurrence of pHPT. A mutation in exon 10 is an independent risk factor for recurrence after STP, and LTSP may not be recommended when exon 10 is mutated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".