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Record W4386046576 · doi:10.57264/cer-2022-0175

Cost–consequence analysis of ofatumumab for the treatment of relapsing-remitting multiple sclerosis in Canada

2023· article· en· W4386046576 on OpenAlexaffabout
Virender Bhan, Fraser Clift, Moogeh Baharnoori, Kimberly Thomas, Barkha P. Patel, François Blanchette, Nicholas Adlard, Umakanth Vudumula, Kapil Gudala, Nikkita Dutta, Daniel Grima, Soukaïna Mouallif, Fatine Farhane

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsNovartis (Canada)EVERSANA (Canada)Queen's UniversityMemorial University of NewfoundlandUniversity of British Columbia
Fundersnot available
KeywordsMedicineOfatumumabNatalizumabRelapsing remittingOcrelizumabMultiple sclerosisPhysical therapyInternal medicineDiseaseImmunologyRituximab

Abstract

fetched live from OpenAlex

Aim: The costs and consequences of initial and delayed ofatumumab treatment were evaluated in relapsing-remitting multiple sclerosis with active disease in Canada. Materials & methods: A Markov cohort model was used (10-year horizon, annual cycle length, 1.5% discounting). Scenario analyses examined ofatumumab as first-line treatment versus 3 and 5 years following switch from commonly used first-line therapies. Results: Ofatumumab resulted in improvements in clinical outcomes (relapses and disease progression) and productivity (employment and full-time work), and reduction of economic burden (administration, monitoring and non-drug costs) that were comparable to other high-efficacy therapies (ocrelizumab, cladribine and natalizumab). Switching to ofatumumab earlier in the disease course may improve these outcomes. Conclusion: Results highlight the value of a high-efficacy therapy such as ofatumumab as initial treatment (i.e., first-line) in newly diagnosed relapsing-remitting multiple sclerosis patients with active disease.

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)0.003
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 score0.121
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
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 head0.463
GPT teacher head0.489
Teacher spread0.026 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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