Bibliographic record
Abstract
This study summarizes up-to-date information about the biopositive effects of low radiation treatment (LRT), radiation hormesis, and our experimental devices. In addition, we present a favorable treatment result in a patient with advanced rectal carcinoma who received LRT at home using a radon gas aspirator. The patient was a 61-year-old man who underwent proctectomy in 2010. During the additional first-line chemotherapy, apparent increases in tumor makers identified multiple remote metastases in the lung, sacrum and liver. Sacrum pain limited his activity of daily living and impeded his coming to our facility for LRT. Then, we decided to provide him home LRT using a radon gas aspirator. He inhaled radon gas for 15 minutes at least 3 times a day at home, resulting in remarkably reduced tumor markers and sacrum pain relief. He could walk, keep sitting up without support and sleep in the spine position again after receiving home LRT. No influence of radon gas inhalation on the second-line chemotherapy was observed. The favorable effects of LRT lead us to believe that the newly-developed devices will provide the clinical significance on malignant diseases. To establish the LRT regimen, further clinical investigation and data accumulation are thus called for.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".