Review of the Clinical and Technical Aspects of<sup>99m</sup>Tc-Dimercaptosuccinic Acid Renal Imaging: The Comeback “Kit”
Bibliographic record
Abstract
99mTc-labeled dimercaptosuccinic acid (99mTc-DMSA) imaging is a well-established and highly sensitive method for the diagnosis of several renal cortical disorders affecting children and adults. Beginning in 2014, 99mTc-DMSA availability was severely impaired when it was added to the Drug Shortages List of the U.S. Food and Drug Administration and was commercially unavailable thereafter. The agent shortage negatively impacted practitioners’ ability to evaluate renal cortical defects in children and adults and changed renal imaging practice. A survey among pediatric nuclear medicine clinicians confirmed the clinical need for 99mTc-DMSA. Finally, in early 2023 the Food and Drug Administration again approved 99mTc-DMSA in the United States. During the 99mTc-DMSA shortage, established practitioners may not have had the opportunity of using 99mTc-DMSA as they were accustomed in their experience. Also, newer imaging specialists and referring physicians and technologists may not have benefited from having 99mTc-DMSA in their training. Therefore, it is time to bring back 99mTc-DMSA into the armamentarium of imaging methods available to evaluate regional cortical renal function.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".