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SPECIAL ISSUE: DESIGNING IMPLANT TRIALSGUEST EDITOR: MOHIT BHANDARIPreface: Designing Implant Trials in 2010: A Recipe for Success

2009· article· en· W2417267483 on OpenAlexaff
Mohit Bhandari

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster UniversityHamilton General Hospital
Fundersnot available
KeywordsClinical trialAuditMedicineFood and drug administrationRegulatory scienceDrug trialClinical study designRandomized controlled trialMedical physicsBusinessRisk analysis (engineering)SurgeryAccounting

Abstract

fetched live from OpenAlex

In a recent evaluation of 1017 device trials listed on a clinical trials register (www.clinicaltrials.gov) between 2005 and 2009, only 84 (8.2%) represented orthopedic device evaluations. These device trials notably had few numbers of patients and few centers and represented approximately 7% of drug trials on the same registry. The relatively small proportion of device trials in orthopedics may represent a lack of interest; however, given the device focus of the field, the answer is more likely to be a lack of necessity—historically, regulatory pathways to implant approvals have not required clinical trials and have largely focused on preclinical and early case-series evaluations. The changing landscape of the regulatory environment necessitates a renewed interest in high-quality clinical research. Specifically, randomized trials of implants in orthopedics are poised to become major designs in the future. Given the general uncertainty in knowledge of regulatory pathways among researchers and health care providers, the current symposium was developed to clarify the design and execution of device trials in a changing regulatory environment. We focus our papers on the Food and Drug Administration (FDA) device classes and regulatory pathways (510K), design challenges in regulatory trials, site audits, and standard operating procedures. In over a decade of conducting trials, we have also realized the critical importance of data-management systems and contract research organizations. Not every clinician, device manufacturer, or researcher planning a randomized trial will have the infrastructure or experience to meet the strict regulatory compliance guidelines for the proper conduct of the trial. Understanding what to look for in a contract research organization is extremely helpful, especially in an environment of limited funding and high expectations. We hope that the current symposium will provide a broad context to clinical trials of orthopedic devices. Despite the challenges of the current regulatory arena, there has never been a more exciting time to conduct research in our field.

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 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)0.099
metaresearch head score (Gemma)0.055
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0990.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.229
GPT teacher head0.466
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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