Theranostics - a sure cure for cancer after 100 years?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancer has remained a formidable challenge in medicine and has claimed an enormous number of lives worldwide. Theranostics, combining diagnostic methods with personalized therapeutic approaches, shows huge potential to advance the battle against cancer. This review aims to provide an overview of theranostics in oncology: exploring its history, current advances, challenges, and prospects. We present the fundamental evolution of theranostics from radiotherapeutics, cellular therapeutics, and nanotherapeutics, showcasing critical milestones in the last decade. From the early concept of targeted drug delivery to the emergence of personalized medicine, theranostics has benefited from advances in imaging technologies, molecular biology, and nanomedicine. Furthermore, we emphasize pertinent illustrations showcasing that revolutionary strategies in cancer management enhance diagnostic accuracy and provide targeted therapies customized for individual patients, thereby facilitating the implementation of personalized medicine. Finally, we describe future perspectives on current challenges, emerging topics, and advances in the field.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it