Evidence map›Paper›PMID 38832091›Full record

ArticleFrontiers in research metrics and analytics2024

Exploring the current state of technology transfer in the United States: perspectives and improvement strategies from the experts.

Jordan Eidlisz, Isabelle von Simson, Gabrielle Gold-von Simson

Abstract read
In one paragraph

Article in Frontiers in research metrics and analytics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jordan EidliszDepartment of Medicine, New York University Grossman School of Medicine, New York, NY, United States.
Isabelle von SimsonDepartment of Liberal Arts, Tulane University, New Orleans, LA, United States.
Gabrielle Gold-von SimsonDepartment of Medicine, New York University Grossman School of Medicine, New York, NY, United States.

Funding

Biomedical Entrepreneurship Skills Development Program for the Advancement of Research TranslationR25DK119114 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI GOLD-VON SIMSON, GABRIELLE · 2019 to 2023
$510k
NIDDK NIH HHS R25 DK119114
6 · The paper itself

Abstract

Technology transfer (TT) is a necessary, yet complex process to convey and disseminate scientific knowledge to the commercial sector. However, multiple barriers in TT can impede commercialization and innovative progress. To cultivate a deeper understanding, we conducted five interviews with strategic, elite leaders in different areas of TT in the United States. Experts shared their perspectives on the current state of TT, what needs improvement, and potential solutions to enhance the TT landscape, with a focus on biotechnology and medical devices. The formation of strong management teams, a comprehension of the regulatory, reimbursement, and funding pathways and policies, and thorough market assessments were noted as key aspects for venture success. Collaboration with Technology Transfer Offices (TTOs), industry experts, and strategic partners are also essential to support academic innovators and guide them throughout the complex commercialization process. There is agreement that a venture should have a defined vision and clear goals with a robust business case for the innovation; early involvement of TTOs is essential. Comprehension of the complexities and key facets of TT, while also streamlining the process, will better position biomedical innovators for success.

Indexed as

biomedical devicesbiotechnologycommercializationentrepreneurshiptechnology transfer

Identifiers

PMID38832091
PMCPMC11144850

What OpenQuestion holds

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Registered trials

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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.