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Record W2012272935 · doi:10.1177/1352458513518259

Disability weight for each level of the Expanded Disability Status Scale in multiple sclerosis

2014· article· en· W2012272935 on OpenAlexfundno aff
Joong‐Yang Cho, Keun‐Sik Hong, Ho Jin Kim, Su‐Hyun Kim, Ju‐Hong Min, Nam‐Hee Kim, Suk-Won Ahn, Min Su Park, Jae-Young An, Byung‐Jo Kim, Woojun Kim

Bibliographic record

VenueMultiple Sclerosis Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersUniversity of British ColumbiaInje UniversityWorld Bank Group
KeywordsExpanded Disability Status ScaleMultiple sclerosisOrdinal ScaleMedicineScale (ratio)StatisticsMathematicsGeographyCartographyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The Expanded Disability Status Scale (EDSS) is the most widely employed ordinal disability scale in multiple sclerosis (MS). However, how far apart the individual EDSS levels are along the disability spectrum has not been formally quantified. OBJECTIVES: The objective of this paper is to generate refined disability weights (DWs) for each of the ordinal EDSS levels. METHODS: We performed the person trade-off (PTO) procedure to derive DWs of five representative EDSS categories (2, 4, 6, 7 and 9), and DWs of the remaining EDSS categories were assigned by linear interpolation. The modified Delphi process was used to achieve consensus among raters. RESULTS: DWs were 0.021 for EDSS 2, 0.199 for EDSS 4, 0.313 for EDSS 6, 0.617 for EDSS 7, and 0.926 for EDSS 9. Panel members achieved a high degree of consensus for each DW, as indicated by low coefficients of variation. CONCLUSIONS: Our DWs confirmed that EDSS is an ordinal scale with highly variable intervals. The availability of DW for each EDSS level allows direct comparison of each MS outcome state with other health states and provides a foundation for the estimation of the disability-adjusted life-years lost of individual patients.

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)0.004
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.249
GPT teacher head0.334
Teacher spread0.085 · 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 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

Citations8
Published2014
Admission routes1
Has abstractyes

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