Nanomedicine: Insights from a Bibliometrics-Based Analysis of Emerging Publishing and Research Trends
Why this work is in the frame
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Bibliographic record
Abstract
Background and Objectives: Nanomedicine, a term coined by the American engineer Eric Drexler (1955) and Robert Freitas Jr. (1952) in the nineties, can be defined as a complex, multi-disciplinary branch of medicine, in which nano-technologies, molecular biotechnologies, and other nano-sciences are applied at every step of disease management, from diagnosis (nano-diagnostics) to treatment (nano-therapeutics), prognosis, and monitoring of biological parameters and biomarkers. Nanomedicine is a relatively young discipline, which is increasingly and exponentially growing, characterized by emerging ethical issues and implications. Nanomedicine has branched out in hundreds of different sub-fields. Materials and Methods: A bibliometrics-based analysis was applied mining the entire content of PubMed/MEDLINE, using “nanomedicine” as a Medical Subject Heading (MeSH) search term. Results: A sample of 6696 articles were extracted from PubMed/MEDLINE and analyzed. Articles had been published in the period from 2003 to 2019, showing an increasing trend throughout the time. Six thematic clusters emerged (first cluster: molecular methods; second cluster: molecular biology and nano-characterization; third cluster: nano-diagnostics and nano-theranostics; fourth cluster: clinical applications, in the sub-fields of nano-oncology, nano-immunology and nano-vaccinology; fifth cluster: clinical applications, in the sub-fields of nano-oncology and nano-infectiology; and sixth cluster: nanodrugs). The countries with the highest percentages of articles in the field of nanomedicine were the North America (38.3%) and Europe (35.1%). Conclusions: The present study showed that there is an increasing trend in publishing and performing research in the super-specialty of nanomedicine. Most productive countries were the USA and European countries, with China as an emerging region. Hot topics in the last years were nano-diagnostics and nano-theranostics and clinical applications in the sub-fields of nano-oncology and nano-infectiology.
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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 | 0.009 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.096 | 0.106 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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