Reduction of downtime and improvement of the utility of a conventional radiotherapy simulator
Bibliographic record
Abstract
The modifications made to a radiotherapy simulator that improve its functionality and minimize its downtime are described. Functionality was enhanced with some simple effective improvements. Decreasing the frequency and time spent on repairs reduced downtime. These "in-house" improvements were made to a Varian Ximatron simulator at the Tom Baker Cancer Center. Now there is easier access to the field light and optical distance indicator (ODI). Projections of the field light and ODI are much brighter and ODI adjustment is more reproducible. A crosshair was etched onto the clear x-ray exit window to optically overlap with the crosshair of the reticule. Now, the time required to replace a field light is up to seven times faster and the time to realign a reticule is reduced to the order of three times faster. Only one quality control (QC) check is required after these adjustments. Removal of excess heat within the gantry head has eliminated two major problems: (A) the metal rod supporting the mirror assembly for the field light no longer expands and contracts causing a misalignment of the field light projection, and (B) the damage to the bearing in the cooling fan assembly has been eliminated. The simulator is now more functional and these improvements have reduced downtime significantly, making repairs and adjustments much more convenient and efficient.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".