Unusual Appearance of Hyperintense Hemangiomata on Tc-99m Colloid SPECT/CT
Bibliographic record
Abstract
A 44-year-old woman was investigated for hepatic steatosis, and underwent Xe-133 and Tc-99m colloid SPECT/CT. The colloid scan demonstrated 2 unexpected foci of hyperaccumulation corresponding to 2 CT-diagnosed hemangiomata. Further evaluation with Tc-99m RBC blood pool SPECT/CT confirmed these 2 lesions as intensely increased on delayed images, confirming hemangiomata. Hemangiomata are the most common benign mesenchymal hepatic tumors, and almost invariably present as defects on nuclear colloidal imaging.1–3,8 This case serves to illustrate that hemangiomata can rarely be hyperintense on colloid imaging.7 It is possible that the Xe-133 confirmed hepatic steatosis was a confounding factor in this patient.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".