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What Is Involved in a Regulatory Trial Investigating a New Medical Device?

2007· article· en· W2056065774 on OpenAlexaff
Paula McKay, Sarah Resendes, Emil H. Schemitsch, Mohit Bhandari

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2007
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsHamilton General HospitalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedical deviceClinical trialProcess (computing)Risk analysis (engineering)MedicineBusinessMedical emergencyComputer science

Abstract

fetched live from OpenAlex

Regulatory trials allow for the clinical evaluation of new drugs and medical devices, determining whether or not they can be safely and effectively used in patient care. The outcome of these trials may result in new and better ways of preventing, diagnosing, and treating illness. However, conducting a regulatory trial to evaluate a new medical device is a complex and time-intensive process involving many parties. This paper will provide an overview of the regulatory approval process for medical devices in the United States and will discuss what is involved in conducting a regulatory trial investigating a new device.

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)0.335
metaresearch head score (Gemma)0.461
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.665
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3350.461
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0030.002
Science and technology studies0.0090.020
Scholarly communication0.0160.023
Open science0.0040.004
Research integrity0.0560.027
Insufficient payload (model declined to judge)0.0080.003

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 head0.039
GPT teacher head0.375
Teacher spread0.336 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
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

Citations2
Published2007
Admission routes1
Has abstractyes

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