The Utility of Multidetector Computed Tomography for Detection of Parathyroid Disease in the Setting of Primary Hyperparathyroidism
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the accuracy of multidetector computed tomography (MDCT) in the detection of parathyroid adenoma and hyperplasia in the setting of primary hyperparathyroidism. METHODS: Records of 48 patients with biochemically confirmed primary hyperparathyroidism, who underwent preoperative imaging with 16- or 64-slice contrast-enhanced MDCT and subsequent successful parathyroidectomy over a 3-year period, were reviewed. Two radiologists, blinded to the operative and histologic findings, independently evaluated multiplanar computed tomographic images for all patients. RESULTS: On pathologic examination, 63 abnormal glands were confirmed in 41 female and 7 male patients (mean age, 63 years). Of the 63 abnormal glands, 40 were adenomatous and 23 were hyperplastic. MDCT demonstrated an 88% (95% confidence interval [CI], 77%-99%) positive predictive value for localizing abnormal hyperfunctioning parathyroid glands. The sensitivity of MDCT in detecting single-gland disease was 80% (95% CI, 68%-92%); whereas the specificity for ruling out hyperfunctioning parathyroid tissue, either adenomatous or hyperplastic, was 75% (95% CI, 51%-99%). The sensitivity for exclusively localizing parathyroid hyperplasia was 17% (95% CI, 2%-33%). The parathyroid adenomas were substantially larger and heavier than their hyperplastic counterparts, with an average weight of 1.51 g (range, 0.08-6.00 g) and 0.42 g (range, 0.02-2.0 g) for adenoma and hyperplasia, respectively. CONCLUSIONS: Contrast-enhanced MDCT demonstrated an 88% positive predictive value for localizing adenomatous and hyperplastic parathyroid glands. The poor sensitivity for detection of multigland disease was likely a result of the smaller size and weight of the abnormal hyperplastic glands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".