COMPARISON OF INTERFRACTIONAL VARIATION IN CANINE HEAD POSITION USING PALPATION AND A HEAD-REPOSITIONING DEVICE
Bibliographic record
Abstract
Radiation treatment planning is performed on images that do not take variation in patient position into account. To compensate for expected variations in position of the patient, a three-dimensional expansion of the clinical target volume, or set-up margin, is added. Variations in patient position can be decreased through use of an immobilization device, allowing selection of a smaller set-up margin. The objective of this prospective study was comparison of interfractional variation in patient position between set-ups of the canine head region using palpation of bony landmarks and set-ups using a head-repositioning device. Fiducial markers were attached to the skull bones of three research dogs, and the dogs were positioned as for a typical radiation treatment of the head region using both set-up methods. A kilovoltage on-board imager was used to acquire orthogonal images and the difference between the x-, y-, and z-axis coordinates of each fiducial marker relative to the initial reference isocenter was measured. The difference in patient position for each axis coordinate was significantly lower for set-ups using the head-repositioning device than for set-ups using bony landmarks (P < 0.05). Ninety-five percent of the absolute values of the displacement vector differences were < 4.62 mm for set-up using bony landmarks, and < 1.93 mm for set-up using the head-repositioning device. A minimum set-up margin of 5-6 mm is recommended when patient set-up is based on bony landmarks and of 2-3 mm when the head-repositioning device is used.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".