Opportunities and Challenges for the Cellular Immunotherapy Sector: A Global Landscape of Clinical Trials
Bibliographic record
Abstract
Global investments in cellular immunotherapies reflect their curative potential. Our landscape of clinical trials will aid developers, investors, adopters and payers in planning for adoption and implementation along realistic time horizons. Trend data enable stakeholders to adapt their business models and capacity to bring immunotherapies to the clinic. For cancer, trends suggest a shift from cancer vaccines to adoptive cellular transfer, alongside a focus on solid tumors. Academic centers, mainly in the USA, lead in early-phase clinical trials and target identification; but industry involvement has increased fourfold over the past two decades. Trends indicate an increasingly personalized approach to onco-immunology, which raises challenges for cost-effective manufacturing and delivery models. Overcoming these challenges provides opportunities for innovative biotechnology firms.
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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 | 0.137 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".