SP-0453 Targeting DNA repair to improve immunesurveillance and restrict cancer growth
Bibliographic record
Abstract
The introduction of new technology has revolutionized the way radiotherapy is delivered for bladder cancer: developing from the use of basic conventional techniques to a high precision, personalized approach.In order to demonstrate that the new technology is beneficial to the patient and to ensure its safe implementation, there is a need to integrate clinical expertise, patient values and research evidence into the development of evidencebased best practices (EBP).Since 2005, a series of prospective and retrospective studies were conducted to investigate different aspects of bladder radiotherapy, such as the accurate definition of treatment volumes, quantification of inter-and intrafractional bladder volumes changes and surface displacements, conformity and normal organ avoidance for dose distributions, mode and frequency of image-guidance interventions, reliability of image guidance surrogates and the cost/benefit of adaptive strategies.At each stage, the impact on geometric and dosimetric precision was quantified as evidence to substantiate the use of EBPs.Currently all bladder patients at our institution are treated with daily image guidance using CBCT with soft tissue alignment, with dose delivered to an individualized clinical target volume and planning target volume using IMRT.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".