ArticleCroatian medical journal2012
Roadmap to personalized medicine.
Article in Croatian medical journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Chronological Analysis of First-in-Class Drugs Approved from 2011 to 2022: Their Technological Trend and Origin.Pharmaceutics · 2023Article
- Proceedings of the Third Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies.Frontiers in neuroscience · 2016Review
- Perceptions and factors affecting pharmaceutical market access: results from a literature review and survey of stakeholders in different settings.Journal of market access & health policy · 2016Article
- EPMA position paper in cancer: current overview and future perspectives.The EPMA journal · 2015Article
- Future of biobanks - bigger, longer, and more dimensional.Croatian medical journal · 2013Article
- The clinic as a good corporate neighbor.Croatian medical journal · 2013Article
- Multi-parameter systematic strategies for predictive, preventive and personalised medicine in cancer.The EPMA journal · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Standard clinical protocols and the concept "one drug fits all" that are currently used to treat illness in many cases are not effective, and strikingly so in the treatment of cancer, where 75% of therapeutic schemes are ineffective. The concept of personalized medicine is that the treatment of the disease is designed on the basis of the individual needs of each patient and the factors that influence their response to different drugs. Individualization of patient care has the potential to generate novel effective therapies, limit the adverse drug effects, create optimal treatments for individual patients, and decrease the cost associated with chronic illness and complications of drug usage. However, to achieve the goals of personalized medicine many challenges must be addressed. Here we discuss possible ways to increase the consistency of data generated by basic research and their suitability for application in medicine. New technologies employing systems biology and computer based approaches will facilitate overcoming many of the scientific challenges in the field. Changes in the education of researchers, health professionals, and the public are also required to successfully implement personalized medicine as a routine in the clinic. Finally, shift of the focus away from the development of blockbuster drugs in the biopharmaceutical industry, and modifications in the legal system to accommodate novel advancements need to be considered. The joint effort of all interested parties is needed to generate an efficient roadmap that will take us rapidly and safely to effective individual treatment, which will eliminate diseases and create better health care for all.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.