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Record W4313464329 · doi:10.22270/ijdra.v10i2.514

A Comparison of the Drug Approval Process in the United States and Canada

2022· article· en· W4313464329 on OpenAlexaboutno aff
Sylvia M. Botros, Mina Botros, Nancy Botros

Bibliographic record

VenueInternational Journal of Drug Regulatory Affairs · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDrug approvalApproved drugFood and drug administrationPopulationClinical trialDrugBusinessFamily medicinePharmacologyEnvironmental health

Abstract

fetched live from OpenAlex

The drug approval process is comparative between the United States and Canada. Without the drug approval process, consumers would be experiencing severe health consequences, therefore, the approval process is expensive, time-consuming, and lengthy for pharmaceutical companies to ensure the compound is safe and effective for its intended use. The electronic databases utilized to recognize applicable published articles from Embase/Ovid and PubMed. The keywords utilized to recognize the pertinent articles were the following: Drug Approval, Drug Development, Food and Drug Administration, United States, Canada, and Pharmaceuticals. The research articles excluded were: non-drug, including vaccines approval process, international regulatory organizations, articles that weren’t relevant to the study, and articles that are not in English language. There was no limit on the date the articles were published. There are more similarities than differences in the drug approval process between Canada and the United States. Both countries contain a regulatory organization (Health Canada; FDA) that review and approve novel drugs to ensure safety and efficacy prior to marketing. Pharmaceutical companies must submit an IND application prior to the inception of clinical trials in humans. Drug approval by FDA is similar to Health Canada, where they develop guidance recommendations to assist pharma companies in complying with regulations. The majority of the published articles focus on the comparison of the drug approval process between the United States and other countries, little-to-no articles discussed the advantages/disadvantages of the drug approval process and how the length of the approval process effects patient population.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.286
Teacher spread0.260 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2022
Admission routes1
Has abstractyes

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