Po‐Thur Eve General‐29: Clinical Implementation of Helical Tomotherapy
Bibliographic record
Abstract
In March 2005 The Ottawa Hospital Regional Cancer Center took delivery of a helical TomoTherapy Hi‐Art machine. We report our experience for installation, commissioning and training for tomotherapy relative to a conventional single energy linac as well as our experiences regarding throughput, process change and implementation of a radically different staffing model. Tomotherapy implementation was faster than a conventional single energy linac (23 vs. 31 days), but additional training requirements for tomotherapy made the overall times comparable (28 vs. 31 days). Presently, the tomotherapy team includes two physicists and two physicians, and dedicates three therapists to tomotherapy per 8 hour shift. The therapists are given responsibility for data transfer, structure contouring, planning and delivery. Mean total effort for treatment preparation per patient is 8.9 hours (median 6.6, range 3.4 to 33). We find daily machine QA for tomotherapy is more demanding than for a conventional linac, requiring approximately 1 hour for machine warm‐up, safety system testing and CT detector calibration. In addition we require approximately 45 minutes of physics time to establish output, energy, and geometric consistency. Overall system performance is verified by a daily delivery QA. The mean overall time for patient setup, MVCT, registration and treatment is 26.5 minutes (median 25.0). Eliminating the MVCT and registration reduces the mean to 18 minutes. For an 8 hour shift we anticipate that a single team (3 therapists) can maintain a patient load of at least 16 patients with daily MVCT and 24 patients with weekly MVCT.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".