Poster — Thur Eve — 52: Clinical use of nanoDots: In‐vivo dosimetry and treatment validation for stereotactic targets with VMAT techniques
Bibliographic record
Abstract
A newly acquired nanoDot In-Light system was compared with TLD-100 dosimeters to confirm the treatment dose in the multiple cases: an electron eye treatment, H&N IMRT and VMAT validation for small targets. Eye tumour treatment with 9 MeV electrons A dose of 1.8 Gy per fraction was prescribed to the 85% isodose. The average dose measured by three TLDs and three Dots was 1.90 and 1.97 Gy. Both detectors overestimated dose, by 2.9% and 6.7% respectively. H&N IMRT treatment of skin cancer with 6 MV photons Dose per fraction is 2.5 Gy. The average doses measured by two TLDs and two Dots were 2.48 and 2.56 Gy, which represent errors of -0.8% and 2.2%, respectively. VMAT validation for small targets using an Agarose phantom, dose 15 Gy A single-tumour brain treatment was delivered using two coplanar arcs to an Agarise phantom containing a large plastic insert holding 3 nanoDots and 4 TLDs. The difference between the average Pinnacle dose and the average dose of the corresponding detectors was -0.6% for Dots and -1.7% for TLDs. A two-tumour brain treatment was delivered using three non-coplanar arcs. Small and large plastic inserts separated by 5 cm were used to validate the dose. The difference between the average Pinnacle dose and the average dose of the corresponding detectors was the following; small phantom 0.7% for Dots and 0.3% for TLDs, large phantom-1.9% for Dots and -0.6% for TLDs. In conclusion, nanoDot detectors are suitable for in-vivo dosimetry with photon and electron beams.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".