Dosimetric scorecards express precise clinical intent: alternate hippocampal-sparing whole-brain RapidPlan models favoring target coverage and homogeneity at 30 and 20 Gy
Bibliographic record
Abstract
Introduction: This study develops two new multi-institutional hippocampal-sparing whole-brain RapidPlan™ models (HLS-EC-WB and HMS-EC-WB) inspired by CCTG-CE.7 featuring enhanced target coverage with varying hippocampal sparing (limited and moderate). Methods: New dosimetric scorecards were created to quantify the models' clinical intent. The models were trained using a multi-institution dataset, and a recursive method was employed to generate consistent, high-quality plans. The models were validated using a five-case set and compared at 20- and 30-Gy prescriptions. Results: Each model scored highest on its associated dosimetric scorecard. The new models achieved higher brain PTV prescription coverage (98%-99%) compared to the previous HSWBv2 model (95.12%), with some trade-off in hippocampal sparing. Conclusions: Three high-quality automated RapidPlan™ models for hippocampal-sparing whole brain are now available, each with a distinct dosimetric scorecard. The new models prioritize increased PTV coverage at some expense to hippocampal sparing. All models, example plans, scorecards, and scoring tools are freely available online.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".