Residual setup error in the canine intracranial region after megavoltage, kilovoltage, or cone‐beam computed tomographic image guidance for radiation therapy
Bibliographic record
Abstract
Sources of residual setup error after image guidance include image localization accuracy, errors associated with image registration, and inability of some treatment couches to correct submillimeter translational errors and/or pitch and roll errors. The purpose of this experimental study was to measure setup error after image-guided correction of the canine intracranial region, using a four degrees-of-freedom couch capable of 1 mm translational moves. Six cadaver dogs were positioned 45 times as for clinical treatment using a vacuum deformable body cushion, a customizable head cushion, a thermoplastic mask and an indexed maxillary plate with a dental mould. The location of five fiducial markers in the skull bones was compared between the reference position and after megavoltage (MV), kilovoltage (kV) and cone-beam computed tomography (CBCT)-guided correction using orthogonal kV images. The mean three-dimensional distance vectors (3DDV) after MV, kV and CBCT-guided correction were 1.7, 1.5 and 2.2 mm, respectively. All values were significantly different (P < .01). The 95th percentiles of the 3DDV after online MV, kV and CBCT-guided correction were 2.8, 2.6 and 3.6 mm, respectively. Residual setup error in the clinical scenario examined was on the order of millimetres and should be considered when choosing PTV margins for image-guided radiation therapy of the canine intracranial region.
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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 | 0.003 |
| 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 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".