Labelling of Red Blood Cells with Technetium-99m for Nuclear Medicine Studies
Bibliographic record
Abstract
ABSTRACT Autologous red blood cells can be labelled with the gamma emitting radionuclide, technetium-99m (Tc-99m) and used for nuclear medicine imaging procedures. Over the last 15 years, the use of Tc-99m red blood cells has ranged from placental and spleen imaging studies to gastrointestinal bleeding and ventricular radiography studies. In vitro, in vivo and modified in vivo methods have been described by several authors to maximize the efficiency of the Tc-99m binding to red cells. The mechanism of radiolabelling likely involves the binding of a reduced form of Tc-99m to intracellular components in the red cell. The amount of stannous ion used as a reducing agent is important in providing maximal labelling. Interactions with various drugs including heparin, doxorubicin, iodinated contrast media, methyldopa, quinidine and digoxin have been reported to interfere with the labelling efficiency. RESUME Le gamma radionucleide emis par le technetium 99M ( 99 Tc) peut etre etiquete avec les globules rouges autologues pour l’utilisation de la visualisation de la medecine nucleaire. Durant les quinze dernieres annees, l’utilisation des globules rouges fixes au 99 Tc a demontre une bonne visualisation de la rate de placenta ainsi que des saignements gastro-intestinaux et ventriculaires. Certains auteurs ont decrit les methodes in vitro , in vivo et in vitro modifiees maximisant l’efficacite de la fixation du 99 Tc reduit aux elements intracellulaires des globules rouges. Il est important de fournir l’etiquetage au maximum a cause du montant d’ions stanneux utilises comme agent de reduction. Quelques interactions medicamenteuses ont ete rapporte avec certaines agents dont l’heparine, la doxorubicine, les produits de contraste iodes, la methyldolpa, la quinidine et la digoxine qui ont interfere avec l’efficacite de l’etiquetage.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".