Poster - Thur Eve - 57: Evaluation of laryngeal mucosal dose with conventional linac and TomoTherapy
Bibliographic record
Abstract
The purpose of this study was to examine whether or not underdosing occurs in the mucosal layer during treatment of glottis cancer. A larynx phantom was produced and regions at risk of recurrence due to suspected underdosing were identified and wells drilled into the phantom for flush placement of TLDs. Seven interest points were chosen. CT simulation was completed prior to the wells being drilled, and again afterwards with the TLD locations indicated using BBs. Treatment plans created for this investigation included: 3DCRT using Elekta-XiO (n=9) and VMAT created using Elekta-Monaco (n=9), both delivered on an Elekta linac; standard TomoTherapy plan (n=11) and a directionally blocked TomoTherapy plan to approximate a 3D-conformal approach (n=5). Imaging dose during TomoTherapy deliveries was accounted for. The average TLD result at each interest point was compared to the planned value using a paired t-test. There was no significant difference between the planned and measured 3DCRT dose (268.9 vs. 267.0 cGy, respectively; p>0.05). Similarly, the planned and measured TomoTherapy treatment did not show any significant differences (271.7 vs 269.7 cGy; p>0.05). In the blocked TomoTherapy plan, significant overdosing was seen (274.5 vs 294.9 cGy; p<0.05) and underdosing was not seen in the VMAT treatment (303.5 vs 321.8 cGy; p>0.05). Further investigation is ongoing to ensure appropriate normalization of results and to investigate the overdosing noted with the blocked TomoTherapy plan. Results from this study suggest that significant underdosing does not occur in the conventional treatment of early glottic cancer using 6MV photons.
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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 | 0.000 | 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) | 0.006 | 0.001 |
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".