Bibliographic record
Abstract
Although systemic radionuclide therapy (SRT) is effective as a palliative therapy in patients with metastatic cancer, there has been limited success in expanding patterns of utilization and in bringing novel systemic radiotherapeutic agents to routine clinical use. Although there are many factors that contribute to this situation, we hypothesize that a better understanding of the radiobiology and mechanism of action of SRT will facilitate the development of future compounds and the future designs of prospective clinical trials. If these trials can be rationalized to the biological basis of the therapy, it is likely that the long-term outcome would be enhanced therapeutic efficacy. In this review, we provide perspectives of the current state of low-dose-rate (LDR) radiation research and offer linkages where appropriate with current clinical knowledge. These include the recently described phenomena of low-dose hyper-radiosensitivity-increased radioresistance (LDH-IRR), adaptive responses, and biological bystander effects. Each of these areas require a major reconsideration of existing models for radiation action and an understanding of how this knowledge will integrate into the evolution of clinical SRT practice. Validation of a role in vivo for both LDH-IRR and biological bystander effects in SRT would greatly impact the way we would assess therapeutic response to SRT, the design of clinical trials of novel SRT radiopharmaceuticals, and risk estimates for both therapeutic and diagnostic radiopharmaceuticals. We believe that the current state of research in LDR effects offers a major opportunity to the nuclear medicine community to address the basic science of clinical SRT practice, to use this new knowledge to expand the use and roles of SRT, and to facilitate the introduction of new therapeutic radiopharmaceuticals.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".