Sci‐Sat AM (2) Therapy‐05: Early Experience with a Clinical TomoTherapy Unit
Bibliographic record
Abstract
In March 2005 The Ottawa Hospital Regional Cancer Center received a helical TomoTherapy Hi‐Art machine, beginning treatment September 2005. The delivery and planning systems were subjected to rigorous daily QA. Herein we report our geometric, dosimetric and uptime analysis. Daily dosimetric QA comprises output and energy checks as well as the geometric consistency of the integrated laser and couch drive systems. At least one patient treatment plan per day is delivered to a phantom for delivery quality assurance, thereby checking all integrated tomotherapy subsystems (e.g. gantry rotation speed, couch speed, dose rate, MLC, jaw calibration and modeling within the planning system). Over the first 130 treatment days the mean output was 0.5% above reference and varied about the mean with a SD of 0.32%. Approximately 1.5% (2 of 132) of output measurements exceeded our tolerance of 2% requiring the calibration to be modified twice prior to the resumption of clinical service. The beam energy was measured by the PDD at 10 and 20 cm. We found these to vary about their expectation values with standard deviations of 0.52% and 0.59% respectively. Single point doses for the Delivery QA were normally distributed with a standard deviation of 2.4%, where 92% of all points were within 3% and 97% of all points were within 5% of expected. All coronal plane film measurements had a distance to agreement of less than 3 mm. We experienced a total of 4.5 days of downtime, 2 of which are attributed to delay in parts delivery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".