Image-guided radiation therapy for post-operative gynaecologic cancer: patient set up verification with and without implanted fiducial markers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Intensity modulated radiotherapy (IMRT) is increasingly being used to treat gynaecological malignancies in the postoperative setting. The purpose of this study was to evaluate the use of image-guided radiotherapy (IGRT) using cone-beam computed tomography (CBCT) with fiducial markers for daily localization. MATERIAL AND METHODS: A single institution study was performed of consecutive cervical or endometrial cancer patients receiving adjuvant external beam radiotherapy (n = 15). Patients were set up at treatment using daily CBCT and alignment of implanted fiducial markers. Image registration was retrospectively completed based on soft tissue matching and the resulting couch shifts from each IGRT method were compared (n = 122). RESULTS: The median shift between IGRT methods was 2 mm, 1 mm and 1 mm in the anterior-posterior (A-P), superior-inferior (S-I), and lateral directions, respectively. The largest deviations were observed in the A-P direction; however, more than 90% were within 5 mm and 63.9% were within 2.5 mm. CONCLUSIONS: IGRT based on soft tissue match provides a noninvasive convenient method for daily localization and is accurate within treatment uncertainty for the majority of cases.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it