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Record W4317757265 · doi:10.12927/hcpap.2023.26993

International Experiences and Made-in-Canada “Social Pharmaceutical Innovations” as Responses to Challenges Facing Drugs for Rare Diseases

2023· letter· en· W4317757265 on OpenAlexaffvenueabout
Conor M.W. Douglas

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2023
Typeletter
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineBusinessEngineering ethicsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Significant challenges are associated with the availability of and access to treatments for rare diseases. In this issue, Sirrs et al. (2023) frame challenges in terms of evidence, economics and ethics and describe how they manifest in the Canadian context. This short response paper argues that although interesting initiatives exist internationally to deal with some of these challenges, a plug-and-play approach will not suffice given the particularities of the Canadian system. Rather than seeking international lessons on how to deal with Canadian challenges, the emerging interdisciplinary framework of social pharmaceutical innovation is advanced here as a whole-systems approach that stands to address interconnected components of the rare disease ecosystem, and in doing so, a made-in-Canada approach is advocated for.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.109
GPT teacher head0.395
Teacher spread0.286 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2023
Admission routes3
Has abstractyes

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