Low Doses of Ionizing Radiation as a Treatment for Alzheimer’s Disease: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: In 2015, a patient in hospice with Alzheimer's disease (AD) was treated with ionizing radiation to her brain using repeated CT scans. Improvement in cognition, speech, movement, and appetite was observed. These improvements were so momentous that she was discharged from the hospice to a long-term care home. Based on this case, we conducted a pilot clinical trial to examine the effect of low-dose ionizing radiation (LDIR) in severe AD. OBJECTIVE: To determine whether the previously reported benefits of LDIR in a single case with AD could be observed again in other cases with AD when the same treatments are given. METHODS: In this single-arm study, four patients were treated with three consecutive treatments of LDIR, each spaced two weeks apart. Qualitative changes in communication and behavior with close relatives were observed and recorded. Quantitative measures of cognition and behavior were administered pre and post LDIR treatments. RESULTS: Minor improvements on quantitative measures were noted in three of the four patients following treatment. However, the qualitative observations of cognition and behavior suggested remarkable improvements within days post-treatment, including greater overall alertness. One patient showed no change. CONCLUSION: LDIR may be a promising, albeit controversial therapy for AD. Trials of patients with less severe AD, double-blind and placebo-controlled, should be carried out to determine the benefits of LDIR. Quantitative measures are needed that are sensitive to the remarkable changes induced by LDIR, such as biological markers of oxidative stress that are associated with AD.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".