Theranostics - a sure cure for cancer after 100 years?
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".