Selective spleen scintigraphy using 99mTc-heat denatured red blood cells in the era of fusion SPECT-CT imaging
Bibliographic record
Abstract
1994 Learning Objectives 1. Compare biodistribution of 99mTc labeled heat-damaged red blood cells (HDRBC), 99mTc sulfur colloid (SC), and undamaged 99mTc RBCs in 14 patients. 2. Contrast biodistribution of 99mTc labeled HDRBC in splenectomised and non-splenectomised patients. 3. Review the clinical role of selective spleen scintigraphy using HDRBC with emphasis on tomographic SPECT and SPECT-CT imaging. Several clinical circumstances call for the specific detection and localization of splenic tissue, amongst them are 1) the evaluation of soft tissue masses where ectopic splenic tissue is suspected, 2) identification of remnant splenic tissue following splenectomy, and 3) surveying of splenic locations in order to characterize congenital anatomic abnormalities. Spleen scintigraphy can be performed using 99mTc labeled HDRBC or SC, the former resulting in more selective organ localization. Heat is known to cause reproducible changes to RBCs including an increase in rigidity, osmotic fragility and spherocytosis, leading to preferential localization of the damaged cells in the spleen. We review clinical data in 14 patients who underwent HDRBC SPECT-CT studies, 9 of whom were previously splenectomised, and compared these with findings in 7 patients who underwent SC spleen SPECT-CT studies and 3 patients who underwent undamaged 99mTc RBCs spleen scintigraphy. Clinical scenarios, qualitative imaging features, and quantitative measurements of uptake are reviewed. Clinical utility of selective spleen scintigraphy in the era of SPECT-CT is highlighted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".