The value of <sup>18</sup>F‐<scp>FDG PET</scp> in pediatric patients with post‐transplant lymphoproliferative disorder at initial diagnosis
Bibliographic record
Abstract
PTLD is a serious complication of both solid organ and BMT. This study assessed whether (18) F-FDG PET, when added to CT scan, had additional value in the initial evaluation of PTLD in pediatric patients and whether PET/CT at baseline can reliably guide biopsy. This retrospective study evaluated 34 consecutive pediatric patients (14 female), aged 3.5-17.0 yr (mean age: 9.9 yr, s.d.: 4.9 yr), who had undergone (18) F-FDG PET/CT from May 2007 to December 2014 at initial diagnosis of PTLD following heart (n = 13), lung (n = 8), kidney (n = 4), liver (n = 3), liver and bowel (n = 3), and bone marrow (n = 3) transplantation. PTLD was diagnosed histopathologically in 33 patients and was based on clinical findings, elevated EBV, and imaging and follow-up results in one patient. On lesion-based analysis, (18) F-FDG PET showed more lesions than conventional CT scan (168 vs. 134), but CT revealed 22 lesions negative on PET. On per patient analysis, PET detected more lesions in 13 patients, CT identified more abnormalities in seven, and both showed the same number of lesions in 14. Adding (18) F-FDG PET to CT scans upstaged the disease in seven patients (20.5%). A combination of (18) F-FDG PET and CT was also useful in guiding biopsy, being positive in 36 of 39 samples (92.3%). These findings indicated that (18) F-FDG PET and CT are complementary at initial staging of pediatric PTLD and that (18) F-FDG PET/CT scanning can guide biopsies.
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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 | 0.003 |
| 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 | 0.001 | 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".