Technical Note: Calculation of normal tissue complication probability using Gaussian error function model
Bibliographic record
Abstract
PURPOSE: The Gaussian error function was first used and verified in normal tissue complication probability (NTCP) calculation to reduce the dose-volume histogram (DVH) database by replacing the dose-volume bin set with the error function parameters for the differential DVH (dDVH). METHODS: Seven-beam intensity modulated radiation therapy (IMRT) treatment planning was performed in three patients with small (40 cm3), medium (53 cm3), and large (87 cm3) prostate volume, selected from a group of 20 patients. Rectal dDVH varying with the interfraction prostate motion along the anterior-posterior direction was determined by the treatment planning system (TPS) and modeled by the Gaussian error function model for the three patients. Rectal NTCP was then calculated based on the routine dose-volume bin set of the rectum by the TPS and the error function model. The variations in the rectal NTCP with the prostate motion and volume were studied. RESULTS: For the ranges of prostate motion of 8-2, 4-8, and 4-3 mm along the anterior-posterior direction for the small, medium, and large prostate patient, the rectal NTCP was determined varying in the ranges of 4.6%-4.8%, 4.5%-4.7%, and 4.6%-4.7%, respectively. The deviation of the rectal NTCP calculated by the TPS and the Gaussian error function model was within +/- 0.1%. CONCLUSIONS: The Gaussian error function was successfully applied in the NTCP calculation by replacing the dose-volume bin set with the model parameters. This provides an option in the NTCP calculation using a reduced size of dose-volume database. Moreover, the rectal NTCP was found varying in about +/- 0.2% with the interfraction prostate motion along the anterior-posterior direction in the radiation treatment. The dependence of the variation in the rectal NTCP with the interfraction prostate motion on the prostate volume was found to be more significant in the patient with larger prostate.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".