Visualisation of Dynamic Surface Data for a Patient Display to Reduce Movement during Radiotherapy
Bibliographic record
Abstract
The accuracy of radiotherapy treatment is dependent on the ability of the patient to maintain a pre-planned, fixed position during each radiotherapy fraction. We present a new visual feedback device that will allow patients to assist in controlling and maintaining their position during both setup and treatment. We use an optical sensor system to gather real-time positional data about the patient during radiotherapy. When the mean-surface, calculated by taking optical data over a number of breathing periods, is subtracted from each sequential surface in a dataset the result is a simple flexing surface lamina. The movement of this lamina about the zero position indicates the deviation from the mean reference surface. This makes it an ideal, intuitive visualisation approach for describing the motion of a patient around their planned setup position. We present our method for determining individually achievable bounds for the patient motion and the use of simple threshold bars and colours to indicate those bounds to the patient. We also present a method for associating a body texture with the mean reference surface and the results of an early demonstration of the device with national patient representatives. Copyright © 2011 ACTA Press.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".