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220 Cost per response for abatacept versus adalimumab in patients with seropositive erosive, early rheumatoid arthritis in the United States, Germany, Spain and Canada

2018· article· en· W2800776653 on OpenAlexaffabout
Jason Foo, José Manuel Rodríguez Heredia, Carlos Polanco Sánchez, Mondher Mtibaa, K Herrmann, E. Alemao, Roelien Postema, Christoph Baerwald

Bibliographic record

VenueLara D. Veeken · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsBristol-Myers Squibb (Canada)
FundersEli Lilly and Company
KeywordsAbataceptMedicineAdalimumabRheumatoid arthritisAntirheumatic AgentsInternal medicineImmunologyRituximab

Abstract

fetched live from OpenAlex

Background: RA is a chronic, inflammatory disorder leading to disability and reduced quality of life. Effective treatment with biologic DMARDs poses a significant economic burden. The AMPLE trial was a head-to-head, randomised study comparing SC abatacept with SC adalimumab. A recent post hoc analysis showed improved efficacy for abatacept in patients with seropositive, erosive early RA (defined as: disease duration ≤6 months, RF or anti-citrullinated protein antibody seropositivity and >1 radiographic erosion) compared with adalimumab. Methods: A previously published decision tree was used to compare the cost per response of abatacept and adalimumab in a cohort of 1000 patients over a 2-year time horizon. Clinical inputs were based on a post hoc analysis of the AMPLE trial in patients with or without seropositive, erosive early RA. Response was based on ACR20/50/70/90 and HAQ-DI. Unit costs for direct medical costs of AEs were based on local tariffs for disease-related groups and the ex-manufacturer price, including mandatory reductions, pay-back and transparent discounts for drugs.

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)0.000
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 score0.346
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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