Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 127Health and Medical Research ImpactsInterdisciplinary Research and CollaborationResearch, Science, and Academia

The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.

Lost in Translation: The Gap in Scientific Advancements and Clinical Application

Open access
Fernandez-Moure, Joseph S.·2016·Frontiers in Bioengineering and Biotechnology
DOI 10.3389/fbioe.2016.00043PMC PMC4891347
Open this paper in CoChat — free accountOpen PDF

Abstract

The evolution of medicine and medical technology hinges on the successful translation of basic science research from the bench to clinical implementation at the bedside. Out of the increasing need to facilitate the transfer of scientific knowledge to patients, translational research has emerged. Significant leaps in improving global health, such as antibiotics, vaccinations, and cancer therapies, have all seen successes under this paradigm, yet today, it has become increasingly difficult to realize this ideal scenario. As hospital revenue demand increases, and financial support declines, clinician-protected research time has been limited. Researchers, likewise, have been forced to abandon time- and resource-consuming translational research to focus on publication-generating work to maintain funding and professional advancement. Compared to the surge in scientific innovation and new fields of science, realization of transformational scientific findings in device development and materials sciences has significantly lagged behind. Herein, we describe: how the current scientific paradigm struggles in the new health-care landscape; the obstacles met by translational researchers; and solutions, both public and private, to overcoming those obstacles. We must rethink the old dogma of academia and reinvent the traditional pathways of research in order to truly impact the health-care arena and ultimately those that matter most: the patient.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fbioe.2016.00043

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Operational challenges for translational research and medicine. Adapted from Blumberg et al. Harvard Catalyst Website (https://catalyst.harvard.edu/). Translational research is an iterative, dynamic, and layered process with several obstacles that must be negotiated (top). These layers include T0, identification of clinical problem followed by preclinical and optimization studies to define material candidates for compound synthesis or cellular mechanisms for intervention; T1, initial Phase I studies in humans that aim to demonstrate proof of concept and safety; T2, Phase II and III clinical trials that allowed for incremental and sequential evaluations and approvals prior to implementation; T3, post-marketing surveillance trials, conducted after the device has been in the market, are used to determine long term efficacy, impact on quality of life, and comparison to other similar devices; and T4, outcomes research to determine the impact of effectiveness intervening on patients in the general community, cost-effectiveness compared to equivalent technologies. Valley of death (red) comprises research related to the T0, T1, and T2 stages (Blumberg et al., 2012).
Figure 1 Operational challenges for translational research and medicine. Adapted from Blumberg et al. Harvard Catalyst Website (https://catalyst.harvard.edu/). Translational research is an iterative, dynamic, and layered process with several obstacles that must be negotiated (top). These layers include T0, identification of clinical problem followed by preclinical and optimization studies to define material candidates for compound synthesis or cellular mechanisms for intervention; T1, initial Phase I studies in humans that aim to demonstrate proof of concept and safety; T2, Phase II and III clinical trials that allowed for incremental and sequential evaluations and approvals prior to implementation; T3, post-marketing surveillance trials, conducted after the device has been in the market, are used to determine long term efficacy, impact on quality of life, and comparison to other similar devices; and T4, outcomes research to determine the impact of effectiveness intervening on patients in the general community, cost-effectiveness compared to equivalent technologies. Valley of death (red) comprises research related to the T0, T1, and T2 stages (Blumberg et al., 2012).CC BY · PubMed Central
Figure 2 Valleys of death. The “valleys of death” concept is used to describe situations where technology failed to reach clinical implementation. Termination of studies in Valley 1 when a technology has shown efficacy, yet is unable to obtain financing to take it to commercialization and human trials. Termination of studies in Valley 2 is due to a rapid decline of funding during the costly human trial phase and occurs during the stage in-licensed technology becomes an actual revenue-generating product.
Figure 2 Valleys of death. The “valleys of death” concept is used to describe situations where technology failed to reach clinical implementation. Termination of studies in Valley 1 when a technology has shown efficacy, yet is unable to obtain financing to take it to commercialization and human trials. Termination of studies in Valley 2 is due to a rapid decline of funding during the costly human trial phase and occurs during the stage in-licensed technology becomes an actual revenue-generating product.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 22 references

Research works printed but not linked: 2

  1. Fuller, 1982, Critical Path
  2. Schacht, 2000, Patent Ownership and Federal Research and Development (R & D): A Discussion on the Bayh-Dole Act and the Stevenson-Wydler Act

Cited by

See all 127

Related

Metadata

Authors
Fernandez-Moure, J. S.
Affiliations
Houston Methodist (US)
Corresponding author
Fernandez-Moure, Joseph S.
ORCID
Fernandez-Moure, Joseph S.
Venue
Frontiers in Bioengineering and Biotechnology
Publisher
Frontiers Media SA
Published
2016-06-03
Volume
4
Pages
43-43
ISSN
2296-4185 (ISSN-L)
DOI
10.3389/fbioe.2016.00043
PMC
PMC4891347
OpenAlex
W2460727754
Cited by
127
References
22
Citations by year
2026: 15 · 2025: 17 · 2024: 21 · 2023: 16 · 2022: 12 · 2021: 11 · 2020: 7 · 2019: 12 · 2018: 11 · 2017: 3 · 2016: 2
FWCI
9.26
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Bioengineering and Biotechnology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health and Medical Research Impacts
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Interdisciplinary Research and Collaboration
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Research, Science, and Academia
Keywords
translational research, clinical application of research, research funding, medical device development, health care financing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.