Assessing the barriers to image‐guided drug delivery
Bibliographic record
Abstract
Imaging has become a cornerstone for medical diagnosis and the guidance of patient management. A new field called image‐guided drug delivery ( IGDD ) now combines the vast potential of the radiological sciences with the delivery of treatment and promises to fulfill the vision of personalized medicine. Whether imaging is used to deliver focused energy to drug‐laden particles for enhanced, local drug release around tumors, or it is invoked in the context of nanoparticle‐based agents to quantify distinctive biomarkers that could risk stratify patients for improved targeted drug delivery efficiency, the overarching goal of IGDD is to use imaging to maximize effective therapy in diseased tissues and to minimize systemic drug exposure in order to reduce toxicities. Over the last several years, innumerable reports and reviews covering the gamut of IGDD technologies have been published, but inadequate attention has been directed toward identifying and addressing the barriers limiting clinical translation. In this consensus opinion, the opportunities and challenges impacting the clinical realization of IGDD ‐based personalized medicine were discussed as a panel and recommendations were proffered to accelerate the field forward. WIREs Nanomed Nanobiotechnol 2014, 6:1–14. doi: 10.1002/wnan.1247 This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".