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Record W2943596749 · doi:10.1007/s41669-019-0137-0

The Costs of Industry-Sponsored Medical Device Clinical Trials in Alberta

2019· article· en· W2943596749 on OpenAlexafffundabout
İlke Akpinar, Arto Öhinmaa, Lars Thording, Dat T. Tran, Richard N. Fedorak, Lawrence Richer, Philip Jacobs

Bibliographic record

VenuePharmacoEconomics - Open · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaAlberta Health Services
KeywordsClinical trialBusinessMedical deviceMedicineInternal medicineBiomedical engineering

Abstract

fetched live from OpenAlex

OBJECTIVE: Our objective was to describe the costs of industry-sponsored clinical trials for medical devices in Northern Alberta, Canada. METHODS: We used centralized data to identify all industry-sponsored medical device clinical trials initiated in Northern Alberta from 2012 to 2016. For each arm of each trial, we calculated the price of devices provided by the sponsor and the cost of clinical and administrative services that were incurred to clinically operationalize the treatment. RESULTS: Our sample consisted of 18 device trials initiated between January 2012 and January 2016. The overall cost (Canadian dollars [$Can], year 2018 values) per enrolee was $Can18,243 for the experimental arm and $Can13,827 for the control arm. Devices were the highest cost component, at $Can13,446 per enrollee in the experimental arm. Clinical costs in the control arms were higher on average ($Can7202 vs. 2504) than those in the experimental arms. CONCLUSION: Data from industry-sponsored clinical trials can provide important information on the full costs of device-related interventions. As device costs rise, and as policy makers require more evidence on device-related treatments, the cost of medical device-driven interventions should be documented along with their effectiveness.

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)0.051
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0150.001

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 head0.678
GPT teacher head0.681
Teacher spread0.003 · 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

Citations5
Published2019
Admission routes3
Has abstractyes

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