Imaging Characteristics of a Novel Technetium Tc 99m–Labeled Platelet Glycoprotein IIb/IIIa Receptor Antagonist in Patients With Acute Deep Vein Thrombosis or a History of Deep Vein Thrombosis
Bibliographic record
Abstract
BACKGROUND: The diagnosis of recurrent deep vein thrombosis (DVT) is challenging. Imaging with radiolabeled peptides offers a new approach for detecting acute DVT. Technetium Tc 99m ((99m)Tc)-apcitide binds with high affinity and specificity to the glycoprotein IIb/IIIa receptors expressed on activated platelets and, therefore, (99m)Tc-apcitide scintigraphy should be negative with residual abnormalities caused by old, inactive thrombi and positive with new, active thrombi. METHODS: In a prospective multicenter study, (99m)Tc-apcitide imaging was performed on 38 patients with a newly diagnosed first DVT (group 1) and 40 patients with previous DVT, symptoms of postthrombotic syndrome, and chronic intraluminal abnormalities on ultrasonography (group 2). Images were interpreted in a blinded fashion by 2 experts and by newly trained nuclear medicine physicians. The sensitivity and specificity of (99m)Tc-apcitide were determined by calculating the proportion of scans in group 1 patients that were read as "positive for acute DVT" and the proportion of scans in group 2 patients that were read as "negative for acute DVT," respectively. RESULTS: When read by 2 experts, ( 99m)Tc-apcitide had a sensitivity of 92% for both readers and specificities of 82% and 90%. Agreement between the experts was excellent. However, the accuracy and interreader agreement for newly trained nuclear medicine physicians were lower. CONCLUSIONS: Technetium Tc 99m-apcitide scintigraphy has potential utility in suspected recurrent DVT because it detects most acute thrombi and has few false-positive results in patients with previous DVT. However, the accuracy appears to depend on the training and experience of the interpreters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".