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Record W7132065799

Evaluation of marketing authorization and clinical implementation of ulipristal acetate for uterine fibroids

2022· article· en· W7132065799 on OpenAlexaboutno aff
M.E. de Lange, Mei‐An Middelkoop, Ben Willem J Mol, Pierre M. Bet, Judith A. F. Huirne

Bibliographic record

VenuePure Amsterdam UMC · 2022
Typearticle
Languageen
FieldMedicine
TopicUterine Myomas and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsUlipristal acetateMarketing authorizationObservational studyAgency (philosophy)AuthorizationClinical trialUterine fibroidsMedical prescription
DOInot available

Abstract

fetched live from OpenAlex

Ulipristal acetate (UPA) is a medical treatment for uterine fibroids and was authorized for surgical pre-treatment in 2012 after the conduct of the PEARL I and II randomized controlled trials and for intermittent treatment after the observational PEARL III and IV trials. However, UPA came into disrepute due to its temporary suspension in 2017 and 2020 because of an apparent association with liver injury. This clinical opinion paper aims to review the process of marketing authorization and implementation of UPA, in order to provide all involved stakeholders with recommendations for the introduction of future drugs. Before marketing authorization, the European Medicines Agency (EMA) states that Phase III registration trials should evaluate relevant outcomes in a representative population, while comparing to gold-standard treatment. This review shows that the representativeness of the study populations in all PEARL trials was limited, surgical outcomes were not evaluated and intermittent treatment was assessed without comparative groups. Implementation into clinical practice was extensive, with 900 000 prescribed treatment cycles in 5 years in Europe and Canada combined. Extremely high costs are involved in developing and evaluating pre-marketing studies in new drugs, influencing trial design and relevance of chosen outcomes, thereby impeding clinical applicability. It is vitally important that the marketing implementation after authorization is regulated in such way that necessary evidence is generated before widespread prescription of a new drug. All stakeholders, from pharmaceutical companies to authorizing bodies, governmental funding bodies and medical professionals should be aware of their role and take responsibility for their part in this process.

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)0.002
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 score0.572
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.051
GPT teacher head0.418
Teacher spread0.367 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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