Translucent poly(vinyl alcohol) cryogel dosimeters for simultaneous dose buildup and monitoring during chest wall radiation therapy
Bibliographic record
Abstract
Chest wall radiation therapy treatment delivery was monitored using a 5 mm thick radiochromic poly(vinyl alcohol) cryogel that also provided buildup material. The cryogels were used to detect positioning errors and measure the impact of shifts for a chest wall treatment that was delivered to a RANDO phantom. The phantom was shifted by ± 2, ± 3, and ± 5 mm from the planned position in the anterior/posterior (A/P) direction; these shifts represent setup errors and the uncertainty associated with lung filling during breath-hold. The two-dimensional absolute dose distributions measured in the cryogel at the planned position were compared with the distributions at all shifts from this position using gamma analysis (3%/3 mm, 10% threshold). For shifts of ± 2, ± 3, and ± 5 mm the passing rates ranged from 94.3% to 95.6%, 74.0% to 78.8%, and 17.5% to 22.5%, respectively. These results are consistent with the same gamma analysis performed on dose planes calculated in the middle of the cryogel and on the phantom surface using our treatment plan-ning system, which ranged from 94.3% to 95.0%, 76.8% to 77.9%, and 23.5% to 24.3%, respectively. The Pinnacle dose planes were then scaled empirically and compared to the cryogel measurements. Using the same gamma metric, the pass rates ranged from 97.0% to 98.4%. The results of this study suggest that cryogels may be used as both a buildup material and to evaluate errors in chest wall treat-ment positioning during deep-inspiration breath-hold delivery. The cryogels are sensitive to A/P chest wall shifts of less than 3 mm, which potentially allows for the detection of clinically relevant errors.
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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 | 0.001 |
| 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 | 0.001 |
| 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".