Exploring the Synergy of Immunotherapy and Conventional Treatments in Cancer Therapy
Bibliographic record
Abstract
Immunotherapy (cancer immunotherapy) is a promising approach to cancer treatment that recognizes and destroys cancer cells by employing immune-related components or by directing the immune system. Between 2017 and 2020, the R&D pipeline for cancer immunotherapies increased by 233%. Immunotherapy can be broadly divided into five broad categories and is suitable for 20 different types of cancer. With the introduction of immune checkpoint inhibitors and CAR-T cell therapies, this approach has revolutionized the way of cancer treatment, even treating some patients with advanced cancers, while not all cancer types respond well to this approach. The effectiveness of immunotherapy as a stand-alone treatment is often constrained by tumor heterogeneity, immune evasion, and resistance mechanisms. To address these issues, immunotherapy is being investigated in combination with other traditional treatment modalities such as radiotherapy, chemotherapy and targeted drugs as a way to improve treatment outcomes. This review explores the rationale behind these combination therapies and discusses the potential of these combined therapies to improve patient survival and quality of life.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".