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Record W4310688617 · doi:10.12927/hcpol.2022.26973

Patent “Evergreening” of Medicine–Device Combination Products: A Global Perspective

2022· article· en· W4310688617 on OpenAlexaffvenueabout
Reed F. Beall, Tali Sara Glazer, Haris Ahmad, Mikayla Buell, Slane Hahn, Adam R. Houston, Aaron S. Kesselheim, Jason Nickerson, Warren Kaplan

Bibliographic record

VenueHealthcare policy · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsBruyèreUniversity of Calgary
Fundersnot available
KeywordsPerspective (graphical)BusinessIntellectual propertyComputer sciencePolitical scienceArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

Background: Patenting medicine-delivery devices (inhalers and pens) is controversial when it extends market protections beyond that of the underlying therapeutic agent. We evaluated how common device patenting is, internationally. Method: Using a product sample (n = 88) and an international patent database, we assessed the issue's scope. Results: When comparing the 88 patent portfolios for each product in each country, Canada was found to be among the most impacted, with 90% of the portfolios containing at least one device patent and 35% of the portfolios containing device patents exclusively. Conclusion: Patenting of delivery devices impacts major pharmaceutical manufacturing centres worldwide. International consensus among stakeholders (regulators and payors) is needed on which device modifications represent meaningful clinical value.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.005
Scholarly communication0.0060.007
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.175
GPT teacher head0.375
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes3
Has abstractyes

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