Radiobiology Video Series. Chapter 07. Individual Radiation Sensitivity and Biomarkers
Bibliographic record
Abstract
In recent years, scientific understanding of the changes radiation makes to the various tissues of the body has vastly increased. Identification of biological markers of radiation exposure and response has become a wide field with an increasing interest across the radiation research community. This chapter introduces the concepts of individual radiosensitivity, radiosusceptibility, and radiodegeneration, which are the key factors to classify radiation responses. Biomarkers are then introduced, and their key characteristics as well as classification are explained, with a particular focus on those biomarkers which have been identified for use in epidemiological studies of radiation risk—as this is a crucial topic of current interest within radiation protection. Brief information on collection of samples is followed by a detailed presentation of predictive assays in use in different settings including clinical applications with responses assessed chiefly in tissue biopsy or blood samples. The sections toward the end of this chapter then discuss the evidence associated with the relationship between age and separately sex, and radiosensitivity, as well as some genetic syndromes associated with radiosensitivity. The final section of this chapter provides a brief summary of how our current knowledge can further support individual, personalized, uses of radiation, particularly in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.572 | 0.352 |
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".