Radiopharmaceuticals for diagnosis in nuclear medicine: a short review
Bibliographic record
Abstract
Radiopharmaceuticals are radioactive compounds which have a bound radionuclide in their structure, whose purpose is directing the radionuclide to a location to be treated or to obtain images. Nuclear medicine is the medical specialty that employs radiopharmaceuticals, which has presented itself as a tremendously useful ally for medicine assisting in various diagnoses and treatments, especially for cancer. The general objective of this work is to identify the main radionuclides and metal complexes currently used as radiopharmaceuticals. The main metal complexes used as radiopharmaceuticals are compounds of technetium (99mTc) like sodium pertechnetate and methylenediphosphonate MDP-99mTc and other compounds of indium (111In), thallium (201Tl), gallium (67Ga, 68Ga), iodine (123I and 131I), chromium (51Cr), sulphur (35S), phosphorus (32P), fluorine (as fluorodeoxyglucose, 18F-FDG and sodium fluorine, Na18F), which are widely used in the nuclear medicine for diagnosis by imaging. They have been of great importance for the early diagnosis of numerous diseases, mainly cancer.Currently, technetium compounds are the majority of radiopharmaceuticals used in all countries. In Brazil, Institute of Energy and Nuclear Research (IPEN) is one of the most important distributors of radiopharmaceuticals, producing, importing and distributing them to clinics and hospitals over the country.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".