SU‐E‐T‐34: Time Series Analysis of Skin Reactions during Heck and Neck IMRT
Bibliographic record
Abstract
Purpose: To quantitatively characterize the time sequence of skin erythema in head and neck patients undergoing intensity modulated radiation therapy (IMRT) treatments using optical reflectance spectroscopy. The overall goal is to identify patients who will develop extreme skin responses earlier than is possible by visual inspection alone. Methods: Ten (10) patients undergoing IMRT for the treatment of head and neck cancers were followed throughout the course of their intervention. Daily spectral skin reflectance measurements were made in order to track changes in the tissue optical properties. Weekly thermoluminescent detector (TLD) readings were performed in the measurement area to verify the dose calculated by the treatment planning system. Patients also completed a weekly questionnaire on factors that may have affected their skin reaction, and were visually inspected by a radiation oncologist.Results: The data were fit by a 2‐component principle component analysis (PCA) model. The preliminary results show that absorbed dose is the largest contributor to changes in the spectral reflectance and that those changes are seen primarily at wavelengths above 600 nm. Differences among patient skin responses were seen and accounted for in the model Conclusions: The PCA model indicates that it may be possible to predict a patientˈs spectral skin reflectance measurement on the final day of treatment given an initial spectrum and the dose. Therefore, further analysis of the data may yield a method of determining patient reactions before visible signs are detected. Partial funding through Varian, Inc. and the Natural Sciences and Engineering Research Council of Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".