The Role of Tc-99m-DTPA Nuclear Medicine GFR Studies in Pediatric Solid Tumor Patients
Bibliographic record
Abstract
BACKGROUND: Technetium-99m-diethylene-triamine-pentaacetate (Tc-99m-DTPA) nuclear medicine studies allow for accurate monitoring of glomerular filtration rate (GFR) in cancer patients receiving chemotherapy. However, these scans may be logistically challenging and are associated with some radiation exposure. OBJECTIVES: The purpose of this study was to retrospectively review the use of Tc-99m-DTPA studies to evaluate the subgroup of children with low GFR by Tc-99m-DTPA who may most benefit from this procedure-namely, the patients with a normal serum creatinine (SCr) and GFR estimated by Schwartz (Sch) calculation who may otherwise not have their renal dysfunction recognized. We further determined how the GFR result modified the treatment plan for these patients. METHODS: Patients aged 2 to 18 years with solid tumors diagnosed from 2000 to 2007 were identified. Tc-99m-DTPA results, corresponding height and SCr, were recorded and GFR Sch calculated. The clinical course of patients with Tc-99m-DTPA <80 mL/min/1.73 m and a normal SCr and GFR Sch were reviewed in detail. RESULTS: Of 714 Tc-99m-DTPA studies in 231 patients, 41 (5.7%) in 24 patients (10.4%) reported a GFR result of <80 mL/min/1.73 m. Of those 41 studies, 16 (39%, 13 patients) were associated with normal SCr and normal GFR Sch. Of these 13 patients, 11 (85%) had 1 or more clinical risk factors suggestive of preexisting renal disease. Three patients had modifications to their chemotherapy. CONCLUSIONS: There were few abnormal Tc-99m-DTPA results reported in this population. The majority of patients with abnormal Tc-99m-DTPA studies and normal SCr had clinical risk factors for renal dysfunction. A future prospective study may better help to define oncology patients for whom Tc-99m are essential for estimating 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".