Poster ‐ 57: Statistical analysis of setup correction for radical IMRT lung patients on a treatment couchtop with six degrees of freedom
Bibliographic record
Abstract
Purpose: To characterize the distributions of setup corrections for radical IMRT lung patients treated on a couchtop with six degrees of freedom (6DOF). Methods: Translational and rotational setup corrections were retrospectively analyzed for patients treated on the HexaPODTM 6DOF couchtop. Conventional and hypo‐fractioned radical IMRT lung treatments were included, for a total of 152 patients and 2,042 fractions. The distribution of setup corrections was analyzed for each DOF. Potential correlations between pairs of corrections were examined and possible causes identified. Intra‐patient variability of setup corrections was characterized. Results: For lateral, longitudinal and vertical setup corrections, the mean ± 1 S.D. (in mm) were: −0.6 ± 3.8, 2.8 ± 5.1, and 1.1 ± 3.9. Each of the three means is statistically significant from the theoretical population mean of zero (p<0.01). For rotations, the means were within 0.1° from zero, with a standard deviation of 1.3°. Moderate correlation (r = −0.45) was observed between longitudinal and pitch corrections, which can be caused by changes in the spinal column position, to which registration is mostly done. The standard deviation for intra‐patient corrections varies among patients: 2 – 10 mm for translations and 0.5 – 3° for rotations. Intra‐patient variability in translations has a positive correlation with that in rotations. Conclusions: In 6DOF setup corrections for lung patients, systematic translational corrections were observed, with moderate correlation between longitudinal and pitch corrections. Careful setups and custom immobilization may help reduce such offsets, correlations, and intra‐patient setup variability.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".