Functional Radiotherapy for Non-Malignant Diseases: The Potential of Ionizing Radiation Beyond Cancer Treatment
Bibliographic record
Abstract
This special issue, "Functional Radiotherapy for Non-Malignant Diseases - The Potential of Ionizing Radiation Beyond Cancer Treatment", addresses one of the oldest yet often overlooked areas of radiation medicine: the therapeutic use of ionizing radiation for non-malignant disorders. More than a century after the first medical use of X-rays, functional radiotherapy for non-malignant diseases is experiencing a renaissance, supported by growing clinical and biological evidence. The articles in this issue span the historical evolution, mechanistic underpinnings, and modern clinical applications of radiotherapy across diverse indications. They address topics such as the biological effects and potential carcinogenic risks of low-dose irradiation, radiotherapy for osteoarthritis and periarticular soft-tissue disorders, benign and premalignant tumors, Graves' ophthalmopathy, and hyperproliferative diseases including Dupuytren's and Ledderhose's disease, among others. Emerging applications such as stereotactic arrhythmia radioablation and functional radiotherapy for neurological or psychiatric disorders further extend its scope. Together, these contributions illustrate how radiotherapy for non-malignant diseases has evolved from empirical practice to a scientifically grounded therapy aimed at functional restoration. In the future, the establishment of standardized protocols, international registries, and prospective clinical trials will be essential to validate efficacy and safety, thereby defining the evolving role of radiotherapy beyond cancer treatment.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".