Radiation-Induced Fibrosis: Mechanisms and Opportunities to Mitigate. Report of an NCI Workshop, September 19, 2016.
Bibliographic record
Abstract
A workshop entitled "Radiation-Induced Fibrosis: Mechanisms and Opportunities to Mitigate" (held in Rockville, MD, September 19, 2016) was organized by the Radiation Research Program and Radiation Oncology Branch of the Center for Cancer Research (CCR) of the National Cancer Institute (NCI), to identify critical research areas and directions that will advance the understanding of radiation-induced fibrosis (RIF) and accelerate the development of strategies to mitigate or treat it. Experts in radiation biology, radiation oncology and related fields met to identify and prioritize the key areas for future research and clinical translation. The consensus was that several known and newly identified targets can prevent or mitigate RIF in pre-clinical models. Further, basic and translational research and focused clinical trials are needed to identify optimal agents and strategies for therapeutic use. It was felt that optimally designed preclinical models are needed to better study biomarkers that predict for development of RIF, as well as to understand when effective therapies need to be initiated in relationship to manifestation of injury. Integrating appropriate endpoints and defining efficacy in clinical trials testing treatment of RIF were felt to be critical to demonstrating efficacy. The objective of this meeting report is to (a) highlight the significance of RIF in a global context, (b) summarize recent advances in our understanding of mechanisms of RIF,
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".