Comparison of manual and computer‐generated customized blocks for whole brain fields used in the treatment of medulloblastoma
Bibliographic record
Abstract
BACKGROUND: By using CT simulation and three-dimensional planning, we have developed a simple but accurate method of producing customized blocks for the cranial fields in the treatment of medulloblastoma. We compare here the margins and the volume of normal tissues included in the treatment volume with those obtained using blocks generated manually. PROCEDURE: Customized blocks for the whole brain field are generated using CT planning and autoblock function. The clinical target volume (CTV) is defined as the whole cerebrospinal fluid pathway, and the whole brain and spinal cord are contoured. A margin of 1.1 cm is generated using the autoblock function to account for set-up errors (3-5 mm) and penumbra (approximately 7 mm). A separate set of blocks was generated manually without the knowledge of the ones generated by the CT-simulator. These 2 sets of blocks were compared for a cohort of 7 consecutive patients. RESULTS: Overall, the manual blocks and the computer-generated blocks were quite similar. Those generated manually had more variations; they were always tighter (median of 6 mm tighter; range: 3-7 mm) at the level of the cribriform plate and in 5/7 patients were more generous (median of 6 mm more generous, range: 0-6 mm) at the temporal lobes. Dosimetric analysis showed that both methods provide adequate coverage of the CTV, with 100% of the CTV receiving > 95% of the prescribed dose for both. CONCLUSIONS: The customized block method for whole brain fields is simple to use and ensures adequate coverage of the target volume.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".