Poster — Thur Eve — 67: Clinical results of deep inspiration breath hold radiation treatment for the left breast patients
Bibliographic record
Abstract
Adjuvant radiotherapy for left breast cancers increases local tumor control, but also increases the risk of radiation-induced cardiac disease. Deep Inspiration Breath Hold (DIBH) can minimize dose to the heart for left breast patients where the heart is within the tangential field. In this study, we evaluated the dosimetric benefit of DIBH technique comparing to free breathing (FB) radiotherapy for left breast cancer patients. Five patients with left breast cancer treated with DIBH technique were selected randomly. The CT scans of breath hold (BH) and FB were taken for every DIBH patient. Standard clinical DIBH intensity-modulated radiotherapy (IMRT) plans were generated with BH scan dataset using the Varian Eclipse TP system. The prescription dose is 4250 cGy in 16 fractions. The BH plan was copied to the FB scan dataset and shifted accordingly to have the same coverage for the breast tissue, and the dose was re-calculated. Dose-volume histograms (DVH) of the heart and lung; mean dose and maximum dose of the heart were calculated and compared from the BH and FB plans for every patient. The lung volume is increased during BH and hence the heart is moved out of the field, resulting in the lower heart maximum dose. The mean dose is almost less than 1 Gy for all BH plans. The average mean heart dose is 0.8 Gy for BH plan compared to 1.6 Gy for FB plan. Patients benefit significantly from DIBH technique due to the very low heart dose.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.010 | 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".