MétaCan
Menu
Back to cohort
Record W4299554288

Barriers to medical device innovation

2014· article· en· W4299554288 on OpenAlexaboutno aff
Bergsl, J, Elle OJ, E Fosse

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness
DOInot available

Abstract

fetched live from OpenAlex

Jacob Bergsland, Ole Jakob Elle, Erik Fosse The Intervention Centre, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway Abstract: The US Food and Drug Administration (FDA) has defined a medical device as a health care product that does not achieve it's purpose by chemical action or by being metabolized. This means that a vast number of products are considered medical devices. Such devices play an essential role in the practice of medicine. The FDA classifies medical devices in three classes, depending on the risk of the device. Since Class I and II devices have relatively simple requirements for getting to the market, this review will focus on “implantable devices”, which, in general, belong to Class III. The European Union and Canada use a slightly different classification system. While early generations of medical devices were introduced without much testing, either technical or clinical, the process of introducing a Class III medical device from concept to clinical practice has become strongly regulated and requires extensive technological and clinical testing. The modern era of implantable medical devices may be considered to have started in the 1920s with development of artificial hips. The implantable pacemaker was another milestone and pacemakers and cardioverters/defibrillators have since saved millions of lives and created commercial giants in the medical device industry. This review will include some examples of cardiovascular devices. Similar considerations apply to the total implantable device market, although clinical and technological applications obviously vary considerably. Keyword: implantable, FDA, regulation, CE-mark, innovation

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.105
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.007
Scholarly communication0.0170.011
Open science0.0040.014
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0630.018

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.

Opus teacher head0.130
GPT teacher head0.503
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicBiomedical and Engineering EducationFrench-language works237,207