ESTRO-ACROP guideline for positioning, immobilisation and setup verification for local and loco-regional photon breast cancer irradiation
Bibliographic record
Abstract
Topic Recommendations Positioning• For most breast cancer treatments supine is the standard position.For patients with larger breasts or patients that require a higher degree of lung sparing, prone can be considered if the equipment and expertise are available.• Both arms up are considered more stable; one arm up may be considered for patients that cannot tolerate both arms up.• When using supine positioning, both flat and elevated board positions are acceptable provided collision risks are managed and the patient is appropriately stabilised.Immobilisation• There is insufficient evidence to support the adoption of any specific immobilisation device of the breast.The pro and cons of specific immobilisation devices must be weighed carefully and evaluated by the local department prior to clinical implementation.Setup• In the absence of surface guided imaging, the use of skin marking is required.• The available options for skin marking should be discussed taking into account long-term patient experience and patient preference.Position verification• Daily 2D-2D or 3D online position verification should be used where feasible.• 2D online/offline position verification is appropriate with consideration of limitations.• Image matching should consider bony anatomy as well as soft tissue displacement/deformation.• SGRT should not replace standard image-guidance without local validation and particular caution to partial-breast/ integrated-boost treatments.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.042 |
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".