Hematological Changes Following Low Dose Radiation Therapy and Comparison to Current Standard of Care Cancer Treatments
Bibliographic record
Abstract
Cancer is the second leading cause of mortality worldwide accounting for almost 10 million deaths in 2020. Current standard of care treatment varies depending on the type and stage of disease, but commonly includes surgery, chemotherapy, and/or radiation therapy. There is evidence that whole- and half-body exposure to low dose ionizing radiation can also be an effective therapeutic due to its stimulation of anti-cancer immunity. One of the limiting factors for past clinical trials using low dose radiation therapy has been adverse hematological events. However, similar hematological changes are also frequently reported following standard of care treatments in oncology. This review summarizes the effects of various cancer therapies on hematologic toxicity through the evaluation of complete blood count reports. The reviewed literature elucidates hematological trends in patients undergoing chemotherapy, and both high and low dose radiation therapy. In general, high dose radiation and chemotherapy can result in widespread changes in blood counts, with the most severe effects related to leukopenia. Overall, compared to standard of care treatments, low dose radiation results in similar, yet more mild hematological changes. Taken together, hematological toxicities should not be a limiting factor in the applicability of low dose radiation as a cancer therapeutic.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".