DOSE MAPPING OF GAMMA IRRADIATION CHAMBER (GIC)
Bibliographic record
Abstract
The use of gamma rays to irradiate blood products have been demonstrated to reduce the probability of post-transfusion graft versus host disease. The aim of this study is to investigate the accuracy of a new batch of Gafchromic EBT-XD films to map the homogeneity of gamma radiation dose delivered to the blood bags and identify the lowest and highest levels of radiation exposure to the blood. The Gammacell 3000 Elan Gamma irradiation chamber (GIC) @ Blood Irradiator with Cesium-137 source was used. During the gamma irradiation at an exposure of 8 minutes 51 seconds to deliver a central dose of 25 Gy to the film, the EBT-XD films were placed at the centre of a blood-equivalent phantom. The doses then were measured using dose mapping analysis of Gafchromic EBT-XD film. The responses of EBT-XD films were compared with EBT3 films and GS 300 Dose Mapping Report 2021 prepared by Best Theratronics Dosimetry Laboratory, Canada. Results showed the value of doses obtained from the mapping using EBT-XD were reliable compared with the doses measured in EBT3 film as they were in the range of the acceptable dose ranges for blood irradiation procedures. The mapping with EBT-XD film yielded a minimum dosage of 18 Gy and a maximum exposure of 32 Gy at 8 minutes 51 seconds of irradiation. In conclusion, EBT-XD offered excellent dosimetric characteristics and has a the potential to dose map the blood irradiator.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".