Comparative analysis of image guidance in two institutions for prostate cancer patients
Bibliographic record
Abstract
AIM/BACKGROUND: The analysis of systematic and random errors obtained from the pooled data on inter-fraction prostate motion during radiation therapy in two institutions. MATERIALS AND METHODS: Data of 6085 observations for 216 prostate cancer patients treated on tomotherapy units in two institutions of position correction shifts obtained by co-registration of planning and daily CT studies were investigated. Three independent variables: patient position (supine or prone), target (prostate or prostate bed), and imaging mode (normal or coarse) were analyzed. Systematic and random errors were evaluated and used to calculate the margins for different options of referencing based on the position corrections observed with one, three, or five imaging sessions. RESULTS: Statistical analysis showed that only the difference between normal and coarse modes of imaging was significant, which allowed to merge the supine and prone position sub-groups as well as the prostate and prostate bed patients. In the normal and coarse imaging groups, the margins calculated using systematic and random errors in the medio-lateral and cranio-caudal directions (5.5 mm and 4.5 mm, respectively) were similar, but significantly different (5.3 mm for the normal mode and 7.1 mm for the coarse mode) in the anterio-posterior direction. The reference scheme based on the first three fractions (R3) was found to be the optimal one. CONCLUSIONS: The R3 reference scheme effectively reduced systematic and random errors. Larger margins in the anterio-posterior direction should be used during prostate treatment on the tomotherapy unit, as coarse imaging mode is chosen in order to reduce imaging time and dose.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".