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Record W3082520350 · doi:10.1186/s13023-020-01519-0

Registries for orphan drugs: generating evidence or marketing tools?

2020· article· en· W3082520350 on OpenAlexaff
Carla E. M. Hollak, Sandra Sirrs, Sibren van den Berg, Vincent van der Wel, Mirjam Langeveld, Hanka Dekker, Robin Lachmann, Saco J. de Visser

Bibliographic record

VenueOrphanet Journal of Rare Diseases · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of British Columbia
FundersAmsterdam University Medical CentersIdorsia PharmaceuticalsRecordati Rare DiseasesAmicus TherapeuticsUltragenyx PharmaceuticalSanofi
KeywordsOrphan drugRare diseasePharmaceutical industryBusinessIndependence (probability theory)Health careDiseaseDrug approvalMedicineDrugPharmacologyBioinformaticsEconomic growthEconomics

Abstract

fetched live from OpenAlex

Independent disease registries for pre-and post-approval of novel treatments for rare diseases are increasingly important for healthcare professionals, patients, regulators and the pharmaceutical industry. Current registries for rare diseases to evaluate orphan drugs are mainly set up and owned by the pharmaceutical industry which leads to unacceptable conflicts of interest. To ensure independence from commercial interests, disease registries should be set up and maintained by healthcare professionals and patients. Public funding should be directed towards an early establishment of international registries for orphan diseases, ideally well before novel treatments are introduced. Regulatory bodies should insist on the use of data from independent disease registries rather than company driven, drug-oriented registries.

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)0.008
metaresearch head score (Gemma)0.059
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.375
GPT teacher head0.415
Teacher spread0.040 · 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
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

Citations28
Published2020
Admission routes1
Has abstractyes

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