Editorial: Non-cellular immunotherapies in pediatric malignancies
Bibliographic record
Abstract
Editorial on the Research Topic Non-cellular immunotherapies in pediatric malignanciesAdvances in pediatric cancer treatment over the past three decades have focused on the intensification of conventional chemotherapy.While this approach was successful, we have exhausted this strategy for future progress in this field.Immunotherapy-using the immune system and its components to combat cancer has received considerable attention for many years with recent advancements elevating the enthusiasm for its potential to advance pediatric cancer care (1, 2).Substantial focus has been placed on cellular therapy, with promising results observed to date (3-6).While investigations in this area continue, there remains a vast array of other strategies in using elements of the immune system that warrants further attention.The immune system has several components which can be harnessed to achieve antitumor responses.Broadly speaking, there are three fundamental strategies that encompass the scope by which non-cellular immunological strategies can combat cancer 1) Using the inherent specificity of the immune system to target specific tumor antigens 2) overriding inhibitory signals to the immune system imposed by the malignant cell, 3) augmenting tumor specific immune responses.Although each area warrants a thorough examination to fully assess its full potential, each varies in terms of both their flexibility and their promise for success. Using the specificity of the immune system to target cancer cellsAntigen specificity is central to why the immune system is attractive as a cancer treatment modality.Antigen specific antibodies have been developed, providing a variety of specifically targeted therapeutic agents."Naked antibodies"-antibodies directed to tumor Frontiers in Immunology frontiersin.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".