CT-Estimated Volume of Wilms Tumor Can Predict Weight
Bibliographic record
Abstract
Wilms tumor weight was used to recruit patients in a recent National Wilms Tumor Study (NWTS) group trial. The authors hypothesized that a simple calculation of tumor volume based on a preoperative CT scan could predict tumor weight. The authors reviewed charts and CT images of patients with Wilms tumors who were treated at their institution between 1985 and 2002. Tumor volume was calculated as: V = 1/6pi x d (long axis) x d (short axis) x d (craniocaudal). Weight and calculated tumor volume were correlated using linear regression. Complete data of tumor weight and volume could be determined in 25 of the 49 patients. These were highly correlated (Spearman R = 0.97). Wilms tumor weight can be predicted based on a simple estimate of tumor volume on a preoperative CT scan. CT-estimated volume may replace weight as a prognostic factor and in guiding management.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".