Does skeletal uptake of MIBI indicate metabolic bone disease
Bibliographic record
Abstract
1021 Objectives: Focal uptake of sestamibi in brown tumors is known, but not diffuse skeletal uptake. This study was performed (i) to identify uptake patterns of sestamibi in bone in different conditions and (ii) to see if this uptake predicts presence of metabolic bone. Methods: We retrospectively reviewed 102 cases with clinical suspicion of primary hyperparathyroidism. 99mTc sestamibi scintigraphy was performed to identify the presence of hyperfunctioning parathyroid tissue. 99mTc MDP bone scan was performed in 14 patients. Results: Of the 102 studies, 27 were reported positive for parathyroid adenoma on scintigraphy. Abnormal bone uptake of sestamibi was seen in 18 / 102 cases (15 with parathyroid adenoma, 2 with secondary hyperparathyroidism and 1 with osteogenesis imperfecta). Skeletal uptake of sestamibi was seen only in the early images, with washout in the delayed phase. Uptake was mostly seen in the sternum and ribs, followed by the clavicles and humeri. Skull uptake was difficult to comment upon due to physiological uptake of sestamibi in the scalp. Bone scan was suggestive of metabolic bone disease in 8/14 cases. Conversely, all patients with metabolic bone disease showed skeletal uptake of MIBI. All 18 patients with skeletal sestamibi uptake showed elevated parathyroid hormone and serum alkaline phosphatase levels. However, not all cases diagnosed as parathyroid adenoma showed skeletal uptake of sestamibi. Cases of parathyroid adenoma with normal alkaline phosphatase levels did not show skeletal uptake of sestamibi. Conclusions: The findings of this study suggest that diffuse skeletal uptake of sestamibi is seen in cases of primary hyperparathyroidism, renal osteodystrophy and osteogenesis imperfecta and may be related to elevated levels of serum alkaline phosphatase.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".