Residual setup error in the canine intracranial region after megavoltage, kilovoltage, or cone‐beam computed tomographic image guidance for radiation therapy
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
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
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.
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 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) | 0.000 | 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