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Record W4245350033 · doi:10.33140/jnh.05.02.01

Regulatory Strategies for Orphan Drug Development in Canada – Australia

2020· article· en· W4245350033 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Nursing & Healthcare · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsOrphan drugPaceIncentiveDrug developmentBusinessRare diseaseMedicinePublic economicsDrugDiseasePharmacologyEconomicsBioinformatics

Abstract

fetched live from OpenAlex

Canada, Australia have evaluated how their governments can facilitate the improvement of scientific merchandise to deal with uncommon issues. Each has hooked up programs and/or policies to help the improvement of merchandise to deal with unmet clinical wishes in small populations and to ensure their citizens get right of entry to such important medicines. Australia’s software, initiated in 1998, changed into evolved in collaboration with the United States Food and Drug Administration to facilitate the alternate and evaluation of facts on orphan tablets. Canada’s evaluation, posted in 1996, determined that a standalone orphan drug software became now not presently warranted, as present regulation and regulatory policies permit early get right of entry to critical medicinal merchandise. The incidences of such diseases were increasing at an extra pace than the speed with which drugs are researched and developed to treat such diseases. One of the fundamental motives is that the pharmaceutical enterprise is not very keen to research the improvement of orphan capsules as those capsules do no longer capture a larger market. This is the modern situation in-spite of the various incentives furnished in the orphan drug act. However, in this article, we’ve tried to focus on present regulatory framework, Current principles of rare sickness, Regulatory Challenges for Rare Disease Drug Development, Regulatory Integrated approach for the improvement and approval of orphan drugs in Canada & 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.517
GPT teacher head0.463
Teacher spread0.053 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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