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Record W4413323873 · doi:10.1016/j.msard.2025.106697

Gaps and discordance in assessment of PIRA identified between HCPs and people with MS: Results of international surveys

2025· article· en· W4413323873 on OpenAlexafffund
Benjamin Greenberg, Gavin Giovannoni, Kerstin Hellwig, Martyna Dackiewicz, Gareth Turley, Jiwon Oh

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsSt. Michael's Hospital
FundersSanofi GenzymeEMD SeronoNational Institutes of HealthGenentechMultiple Sclerosis SocietyModernaKiniksa PharmaceuticalsBiogenMerck KGaASanofiFondation Brain CanadaRegeneron PharmaceuticalsTeva Pharmaceutical IndustriesVir BiotechnologyEli Lilly and CompanyMultiple Sclerosis Society of CanadaAmgen
KeywordsMedicineMultiple sclerosisFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: Progression independent of relapse activity (PIRA) is a major contributor to long-term disability accumulation in multiple sclerosis (MS). This study aimed to explore the assessment of PIRA in clinical practice from the perspectives of people living with MS (pwMS) and healthcare providers (HCPs). METHODS: Cross-sectional surveys were conducted among 310 pwMS and 360 HCPs involved in MS care across seven countries in North America and Europe. RESULTS: HCPs proved to be more motor-focused, primarily through neurological examination and EDSS (75 %), whereas pwMS reported fatigue as the domain most affected (67 %), which was the least assessed domain by HCPs (31 %). 54-61 % of pwMS indicated that the thoroughness, average time spent, and frequency of PIRA assessment had remained the same since diagnosis. As reported by HCPs, roughly 40 % of PIRA assessment remained the same in pwMS with even moderate-severe disability, likely due to time constraints, considered the most limiting factor to measuring PIRA, as well as the lack of a comprehensive, standardized approach and sensitive tools to measure disability as reported by HCPs accurately. CONCLUSION: MS care necessitates a standardized and time-sensitive approach for assessing disability in the absence of relapse, to optimize care and enhance routine disability assessment and monitoring.

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 score0.019
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.304
Teacher spread0.276 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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