Po‐Thur Eve General‐37: Preliminary Analysis of a Cobalt‐60 Beam Under a MIMiC
Bibliographic record
Abstract
Tomotherapy is a novel implementation of IMRT in which conformal dose is delivered by a fan beam of radiation that is modulated by an MLC as it revolves about a patient. The treatment geometry has the added advantage of enabling megavoltage CT (MVCT) imaging of the patient for setup verification immediately prior to treatment. Linac‐based tomotherapy units are now being rapidly adopted for clinical use. For a number of years we have been investigating the potential of using Cobalt‐60 (Co‐60) as the radiation source in tomotherapy. The ultimate goal of Co‐60 based tomotherapy is to provide the dosimetry benefits of tomotherapy while reducing the technical requirements needed to run and maintain a tomotherapy unit thus increasing the availability of tomotherapy worldwide. Previous studies with a benchtop Co‐60 unit have confirmed that conformal IMRT dose delivery is readily achievable with radiation from a Co‐60 source. Essentially, delivery concerns from beam characteristics such as wider penumbra and reduced penetration (compared to a linac beam) become insignificant in the rotational tomotherapy context. Recently we have extended our studies to a more clinically relevant implementation by adding a fan beam multi‐leaf collimator (the NOMOS MIMiC MLC) to our MDS Nordion T‐780C Co‐60 unit for serial tomotherapy. In this presentation we will discuss the challenges created by using Co‐60 for tomotherapy, present some early commissioning data and examine the subsequent steps needed to certify the unit for clinical use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".