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Record W2049617080 · doi:10.2147/mder.s43369

Barriers to medical device innovation

2014· review· en· W2049617080 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMedical Devices Evidence and Research · 2014
Typereview
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsMilestoneFood and drug administrationMedical deviceProduct (mathematics)MedicineClinical PracticeProcess (computing)Risk analysis (engineering)BusinessComputer scienceBiomedical engineeringFamily medicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.063
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.301
GPT teacher head0.569
Teacher spread0.267 · 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