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Record W3013511813 · doi:10.2147/jpr.s236957

<p>Predicting EQ-5D-5L Utility Scores from the Oswestry Disability Index and Roland-Morris Disability Questionnaire for Low Back Pain</p>

2020· article· en· W3013511813 on OpenAlexafffund
Thomas G. Poder, Nathalie Carrier

Bibliographic record

VenueJournal of Pain Research · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsInstitut Universitaire en Santé Mentale de QuébecCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de Sherbrooke
FundersFonds de Recherche du Québec - Santé
KeywordsOswestry Disability IndexMedicineEQ-5DQuality of life (healthcare)Physical therapyLow back painUnivariateBayesian multivariate linear regressionUnivariate analysisMultivariate analysisMultivariate statisticsLinear regressionHealth related quality of lifeStatisticsDiseaseInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cost utility analysis is important for measuring the impact of chronic disease and helps clinicians and policymakers in patient management and policy decisions, but generic preference-based measures are not always considered in clinical studies. OBJECTIVE: To evaluate if health-related quality-of-life (HRQoL)-specific questionnaires used in chronic low back pain (CLBP) can predict EQ-5D-5L utility scores. METHODS: The data come from an online survey on low back pain conducted between October 2018 and January 2019. Health utility scores for EuroQol Five Dimensions Five Levels (EQ-5D-5L) were calculated with the recommended model of Xie et al. The EQ-5D-5L health states ranged from -0.148 for the worst (55555) to 0.949 for the best (11111). Univariate and multivariate linear regression were performed to predict EQ-5D-5L with Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RMDQ) and clinical variables. RESULTS: Analyses were performed in 408 subjects who completed the questionnaires EQ-5D-5L, ODI or RMDQ. Median (range) of EQ-5D-5L was 0.622 (-0.072 to 0.905). There was high correlation between EQ-5D-5L and ODI (r=-0.78, p<0.001), while it was moderate with RMDQ (r=-0.62, p<0.001). The multivariate model to predict EQ-5D-5L with ODI explained 67.6% of variability, and the correlation between actual and predicted EQ-5D-5L was 0.82. Principal predictors were ODI, duration of LBP, invalidity, health satisfaction (0-10 cm), life satisfaction (0-10 cm), and intensity of pain today (0-10 cm). CONCLUSION: Data from this study demonstrated that individual correlation between ODI and EQ-5D-5L was high, but moderate with RMDQ. Correlations between actual and predicted EQ-5D-5L from multivariate models were higher and very high. Considering these results, the multivariate model can be used in similar studies for patient with CLBP to estimate the utility scores from the ODI when the EQ-5D-5L was not measured.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.360
GPT teacher head0.440
Teacher spread0.080 · 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 source (direct Gemma or distilled Codex), 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".

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Citations12
Published2020
Admission routes2
Has abstractyes

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