Gene Transfer Therapy: A Survey of Clinical Trials for Treatment of Various Cancers
Bibliographic record
Abstract
Gene therapy is emerging as a promising approach for treatment of cancers with minimal side effects compared to conventional chemotherapeutic treatments The trends in gene therapy treatment of cancers have been summarized in Clinical trials are ongoing and many successful outcomes have been reported worldwide [2 -7]. Gene transfer therapy is one of the three types of gene therapy for treatment of a variety of cancers. It is relatively a recent approach which introduces new genes into cancerous cells or the surrounding cancerous tissues to cause their death or to slow down their growth. While there are papers describing specific clinical trials of gene transfer therapy for treatment of cancers, the author did not find a comprehensive survey and review of the clinical trials of gene transfer therapy for treating various cancers. In order to address this gap, this paper surveys the clinical trials that have been undertaken till 2015 and summarizes the findings. The summaries that are provided in this paper include geographic distribution of the trials, indications addressed, vectors used, gene types transferred and the outcomes reported. Using relevant statistical tests on the number of clinical trials, papers published and the outcomes reported for each type of cancer, an attempt has been made to identify the types of cancers for which gene transfer therapy treatment is most commonly used.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".