A Novel Method to Verify Gamma Knife Radioactive Source Count
Bibliographic record
Abstract
Background: Regulation requires periodic reconfirmation of the number of radioactive sources in Gamma Knife (GK). To comply with regulations, the total dose output (TDO) method, originally designed to verify amount of radiation received by patients, was adopted as a tool for source security. However, we suspect the TDO method may be unreliable in detecting small numbers of missing sources given that a 2% margin of error is allowed. We propose an alternate method using Gafchromic films to identify number of sources present. Method: To test the efficacy of the TDO method, we simulated real-life source loss by plugging up to 5 sources. TDO after plugging 0, 1, 2, 3, 4, and 5 sources were measured using GK 4C. Percentage change for each additional source plugged was computed. We then proceeded to test the film method. For GK 4C, 2 Gafchromic RTQA2 single sided films measuring 10 × 10 inch were first halved to 5 × 10 inch films. Four 5 × 10 inch films were then adhered to the inner surface of the 18 mm collimator. For GK Perfexion, 2 Gafchromic EBT3 double sided films measuring 10 × 12 inches were combined and rolled into a cone film and adhered to the collimator during installation. In both cases, the films were exposed to 0.5 Gy. Results: TDO was inversely related to the number of source plugged. TDO decreased by more than 2% after 5 plugs. The film method was able to outline all 201 and 192 Co sources for GK4Cand Perfexion respectively. Conclusion: A single lost source is hard to detect using the TDO method given that at least 5 sources had to be lost before the change in TDO will exceed the margin of error (±2%). The film method bypasses this limitation and offers an easy alternative to accurately obtain the number of sources.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.004 | 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".