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Record W2988982017 · doi:10.1177/0020731419886526

Does an Orphan Drug Policy Make a Difference in Access? A Comparison of Canada and Australia

2019· article· en· W2988982017 on OpenAlexaffabout
Joel Lexchin, Nicholas Moroz

Bibliographic record

VenueInternational Journal of Health Services · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsOrphan drugGovernment (linguistics)Food and drug administrationDrugBusinessMedicinePolitical scienceEnvironmental healthPharmacology

Abstract

fetched live from OpenAlex

Canada has been discussing whether to implement an orphan drug policy for more than 25 years. Recently, the federal government announced funding for orphan drugs starting in 2022, and the Canadian Senate has recommended that the country develop an orphan drug policy. This paper uses a list of orphan drugs approved by the United States Food and Drug Administration between 2008 and 2017, inclusive. It then compares Canada, which has no orphan drug policy, and Australia, which has had such a policy since 1997. There was no difference between the countries in the proportion of orphan drugs approved, the time drugs spent in the regulatory review process, and any delay in marketing the drugs in the respective countries compared to the United States. Both Canada and Australia approved virtually all of the drugs that offered a moderate to significant therapeutic improvement. If Canada hopes to provide faster access to orphan drugs, especially those that are therapeutically innovative, it will need to develop a policy that is significantly different from that in Australia.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.063
GPT teacher head0.392
Teacher spread0.330 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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