Biochemical Metrics for Parathyroid Scintigraphy in the Pre-Surgical Evaluation of Hyperparathyroidism
Bibliographic record
Abstract
PURPOSE: To correlate parathyroid scintigraphy results with patient biochemistry, surgery, and pathology to inform appropriateness criteria and assess biochemical metrics in the imaging workup of hyperparathyroidism. METHODS: This retrospective study included 421 patients who underwent parathyroid scintigraphy. Patients were grouped based on primary versus secondary hyperparathyroidism, and clinical profiles were reviewed for scan result, blood work, surgical results, and pathology. Performance metrics of scintigraphy were analyzed. Demographics, bloodwork, and location were compared between positive and negative scans. Predictors of positive scans were identified by multivariate logistic regression analysis. The performance of biochemistry to predict scan results was evaluated by ROC analyses. RESULTS: Positive tests-occurring in 52% of patients-were associated with higher parathyroid hormone (PTH) and corrected calcium. However, PTH was only predictive of a positive test in patients with secondary hyperparathyroidism. On multivariate analysis, male sex, corrected calcium, and younger age were predictors of a positive scan. Corrected calcium was the most predictive with an OR of 1.28 for every 0.1 mmol/L increase. Based on ROC analysis, corrected calcium had an AUC of 0.628 and a cutoff of 2.65 mmol/L maximized sensitivity (88%) and specificity (35%) for a positive test. CONCLUSION: In this large retrospective cohort, several biochemical metrics, including corrected calcium levels, were predictive of a positive scintigraphy study. Furthermore, biochemistry, including PTH levels, significantly differed between primary and secondary hyperparathyroidism suggesting that tailored biochemical metrics are required. This work sets a foundation for the development of a robust biochemical scoring system to optimize patient selection for parathyroid imaging.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".