Incremental value and clinical impact of neck sonography for primary hyperparathyroidism: a risk-adjusted analysis
Bibliographic record
Abstract
BACKGROUND: Despite the different preoperative imaging modalities available for parathyroid adenoma localization, there is currently no uniform consensus on the most appropriate preoperative imaging algorithm that should be routinely followed prior to the surgical management of primary hyperparathyroidism (PHPT). We sought to determine the incremental value of adding neck ultrasonography to scintigraphy-based imaging tests. METHODS: In a single institution, surgically naive patients with PHPT underwent the following localization studies before parathyroidectomy: 1) Tc-99m sestamibi imaging with single photon emission computed tomography/computed tomography (SPECT/CT) or Tc-99m sestamibi imaging with SPECT alone, or 2) ultrasonography in addition to those tests. We retrospectively collected data and performed a multivariate analysis comparing group I (single study) to group II (addition of ultrasonography) and risk of bilateral (BNE) compared with unilateral (UNE) neck exploration. RESULTS: Our study included 208 patients. Group II had 0.45 times the odds of BNE versus UNE compared with group I (unadjusted odds ratio [OR] 0.45, 95% confidence interval [CI] 0.25-0.81, p = 0.008). When adjusting for patient age, sex, preoperative calcium level, use of intraoperative PTH monitoring, preoperative PTH level, adenoma size, and number of abnormal parathyroid glands, Group II had 0.48 times the odds of BNE versus UNE compared with group I (adjusted OR 0.48, 95% CI 0.23-1.03, p = 0.06). In a subgroup analysis, only the addition of ultrasonography to SPECT decreased the risk of undergoing BNE compared with SPECT alone (unadjusted OR 0.40, 95% CI 0.19-0.84, p = 0.015; adjusted OR 0.38, 95% CI 0.15-0.96, p = 0.043). CONCLUSION: The addition of ultrasonography to SPECT, but not to SPECT/CT, has incremental value in decreasing the extent of surgery during parathyroidectomy, even after adjusting for multiple confounding factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".