Immune blockade inhibitors and the radiation abscopal effect in gastrointestinal cancers
Bibliographic record
Abstract
The field of tumor immunology has produced in the recent years a revolution in cancer therapeutics putting an end in the long lasting frustration of investigators in the area stemming from largely unsuccessful strides to develop cancer vaccines. This progress has come from the introduction of immune checkpoint inhibitors, monoclonal antibodies blocking ligand/receptor pairs with inhibitory effects for immune cells. Through this blockade immune checkpoint blockers are able to activate the immune system and create an anti-tumoral effect. A significant sub-set of patients with various types of cancers such as melanoma, lung carcinomas and urothelial cancers benefit from treatment with these drugs and survivals have improved in some cases. However other cancers are primarily resistant to immune blockers and secondary resistance is also the norm. Radiation therapy is often used in the palliative treatment of patients with advanced cancers and, in addition to the local effect in the irradiated field, it may in rare cases produce a systemic antitumor effect, termed "abscopal". This effect has been suggested to be produced by immune mechanisms. Thus an opportunity presents for a synergistic effect of immune stimulation between radiation and immune blockade inhibitors. The therapeutic opportunities presented with the combination of radiation and these drugs for gastrointestinal cancers will be discussed in this editorial overview.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".