The importance of standardization and challenges of dosimetry in conventional preclinical radiation biology research
Bibliographic record
Abstract
To fully exploit the prospects presented by the increasing focus on biological approaches for enhancing radiotherapy outcomes, improvements in repeatability and translatability of radiobiological and preclinical studies are required. This requires the development and adoption of appropriate dosimetric standards and reproducible approaches to increase confidence in the studies, enabling inter-laboratory validation and facilitating clinical translation. An Institute of Physics and Engineering in Medicine (IPEM) working party reviewed the current status and challenges associated with dosimetry of medium-energy X-rays and make recommendations with the aim to optimize the potential clinical significance of radiobiological preclinical investigations. The paper discusses the currently available resources with technical recommendations for performing dosimetry in medium-energy X-rays, along with the consequences of lack of standardization and implications of dose inhomogeneity. It is clear that there is still a gap in understanding the needs for standardization of dosimetric aspects of preclinical and radiobiological studies. It is recommended that these radiobiology studies should be conducted in partnership with medical/radiation physicists. This collaboration ensures the correct utilization of suitable dosimetry systems, thus guaranteeing accuracy and consistency of dose delivery. Appropriate calibration and traceability to national/international standards laboratory, along with regular quality assurance of radiation devices, are paramount to reproducibility. Additionally, it is critical that experimental details and associated dosimetry are sufficiently reported to ensure accurate replication that enables reanalysis including evaluation of dose distributions. Increasing awareness among the researchers and the funding bodies was identified as a crucial step to improve translatability and appropriate resources are budgeted to increase the value for money of research proposals. The proposed recommendations will serve as a vital resource for researchers, encouraging uniformity in experimental design and improving the translatability of preclinical research to clinical settings.
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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 | 0.361 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".