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Record W4392580932 · doi:10.1007/s43441-024-00624-7

Industry Perceptions and Experiences with the Access Consortium New Active Substance Work-Sharing Initiative (NASWSI): Survey Results and Recommendations

2024· article· en· W4392580932 on OpenAlexafffundabout
Gaia Geraci, Robert Smith, Alison Hansford, Eric Johnsson, Lama Abi Khaled, Laura L. King, Michelle Cheng, Tanja Colin, Tse Siang Kang

Bibliographic record

VenueTherapeutic Innovation & Regulatory Science · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsInnovative Medicines Canada
FundersAssociation of the British Pharmaceutical IndustryState Key Laboratory of Synthetical Automation for Process IndustriesInnovative Medicines Canada
KeywordsHarmonizationBusinessMarket accessWork (physics)Transparency (behavior)Flexibility (engineering)PharmacyPublic relationsMarketingMedicinePolitical scienceFamily medicineManagementEngineering

Abstract

fetched live from OpenAlex

The Access Consortium New Active Substance Work-Sharing Initiative, or "Access" for simplicity, allows regulatory authorities (RAs) of the Access Consortium countries to jointly review applications for the registration of new active substances or for new indications. Using a survey developed by the pharmaceutical industry trade associations of the five Access Consortium countries-Australia, Canada, Singapore, Switzerland, and the United Kingdom (UK)-this study gathered insights into the perceptions and experiences of the Access pathway held by affiliates of pharmaceutical companies. Understanding industry perceptions of Access is important for the success of the initiative, as participation is voluntary. Findings indicate that affiliates who participated in Access had mostly positive experiences with this pathway; most affiliates were satisfied with their interactions with the Access RAs and appeared willing to continue to participate in the initiative. Affiliates' reasons for not having yet participated in Access included a lack of opportunity to do so and perceived barriers, such as the Access pathway being too complicated to manage. Recommendations to improve Access cover six key areas: ensure predictability, increase guidance and transparency, streamline processes, maintain flexibility, increase harmonization, and advance RA-industry cooperation. This study should facilitate informed discussions among relevant stakeholders on how to improve Access to maximize efficiencies, accelerate approvals, and improve patient access to innovative medicines.

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 score0.893
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.185
GPT teacher head0.363
Teacher spread0.178 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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