Setup error with and without image guidance using two canine intracranial positioning systems for radiation therapy
Bibliographic record
Abstract
Daily image guidance reduces inter-fractional variation in patient position for intracranial radiation therapy. However, the ability to detect and correct positioning errors is limited below a certain level. Because of these limitations, the accuracy achieved with a positioning system prior to image guidance may affect the error remaining after image guidance (the residual setup error). The objective of this study was to compare the setup accuracy achieved before and after megavoltage (MV) and cone-beam computed tomography (CBCT) guidance between two intracranial positioning systems. Equipment included a four degrees-of-freedom couch capable of 1 mm translational moves. Six dog cadavers were positioned 24 times as for clinical treatment in a head re-positioner (HPS), and the coordinates of five fiducial markers were measured before and after image-guided correction. The values obtained for the HPS were compared with those previously reported for the standard positioning system (SPS) used at this facility. The mean three-dimensional distance vector (3DDV) was lower for the HPS than for the SPS when no image guidance was used (P = .019). The mean 3DDV after MV guidance was lower for the HPS than for the SPS (P = .027), but not different after CBCT guidance (P = .231). The 95th percentiles of the 3DDV after MV and CBCT guidance were 2.1 and 2.9 mm, respectively, for the HPS, and 2.8 and 3.6 mm for the SPS. The setup error after MV guidance was lower for the positioning system that achieved a more accurate patient position before image guidance.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".