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Record W4212822641 · doi:10.1007/s40744-022-00429-w

Post Hoc Analysis of Predictors of Clinical Response to Repository Corticotropin Injection in Persistently Active Rheumatoid Arthritis

2022· article· en· W4212822641 on OpenAlexfundno aff
Roy Fleischmann, Kyle Hayes, S.H. Ahn, George J. Wan, Mary Panaccio, Daniel Karlsson, Daniel E. Fürst

Bibliographic record

VenueRheumatology and Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
FundersMallinckrodt Pharmaceuticals
KeywordsMedicinePost-hoc analysisInternal medicineRheumatoid arthritisErythrocyte sedimentation ratePlaceboPost hocDemographicsClinical trialOsteoarthritisGastroenterologyPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Gel) in patients with refractory rheumatoid arthritis (RA) that was nonresponsive to standard-of-care therapies. The objective of this post hoc analysis was to identify baseline demographics and clinical characteristics that may be predictors of response to RCI. METHODS: The phase IV trial was a two-part, randomized, placebo-controlled withdrawal study. Post hoc analysis was conducted with the open-label portion of the trial data, in which all 258 subjects received RCI (80 U) twice weekly for 12 weeks. Responders were subjects who achieved low disease activity (LDA) by a Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) of < 3.2 at week 12. Responders were compared with nonresponders by assessing the proportion of subjects in each group for demographics and clinical characteristics, including weight, disease duration, medical history including osteoarthritis and unrelated joint conditions, hemoglobin A1c, C-reactive protein, ESR, DAS28-ESR, Clinical Disease Activity Index (CDAI), depression, anxiety, tender joint count (TJC), and swollen joint count (SJC). Bivariate analysis followed by multiple logistic regression analysis were conducted to identify significant baseline predictors for the outcome of achieving LDA by week 12. RESULTS: Bivariate analysis showed that RCI responders had significantly lower baseline TJC (p = 0.0310), SJC (p = 0.0018), ESR (p = 0.0487), and CDAI (p = 0.0112) and shorter RA disease duration (p = 0.0446). Subjects were less likely to achieve LDA if they had osteoarthritis (p < 0.0001), other joint-related conditions unrelated to RA (p < 0.0001), anemia (p = 0.0132), depression (p = 0.0006), or prior or concomitant use of targeted-synthetic or biologic disease-modifying antirheumatic drugs (p < 0.0001). Multiple logistic regression analysis revealed that, of the above, only ongoing osteoarthritis (p = 0.0272) or other joint-related conditions (p = 0.0193) were significant negative predictors of RCI response. CONCLUSIONS: These results identify specific patient characteristics that may be considered predictors of positive or negative clinical response to RCI.

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 score0.091
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.016
GPT teacher head0.306
Teacher spread0.290 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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