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Record W776000467

A Comparison of Three Low Back Disability Questionnaires With Rasch Analysis

2011· article· en· W776000467 on OpenAlexaboutno aff
Gyoung‐mo Kim, So-Yeon Park, Chung‐Hwi Yi

Bibliographic record

VenuePhysical Therapy Korea · 2011
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsRasch modelPhysical therapyLow back painPsychologyPsychosocialMedicinePsychiatryDevelopmental psychologyAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to review existing assessment tools for patients with low back pain and improve them through combination. A total of 314 patients with low back pain participated. Their condition was assessed using the Oswestry Disability Questionnaire (ODQ), the Quebec Back Pain Disability Scale (QBPD), and the Back Pain Functional Scale (BPFS). Rasch analysis was applied to identify inappropriate items, item difficulties, and the separation index. In this study, the 'sex life' item of the ODQ (10 items) and the 'sleeping' item of the BPFS (12 items) showed misfit statistics, whereas all items of the QBPD (20 items) were appropriate. After combining the ODQ, QBPD and BPFS, Rasch analysis was applied. The 'pain intensity', and the 'sex life' item of the ODQ and the 'throw a ball' item of QBPD showed misfit statistics. These 3 items were retained for further analysis. The remaining 42 combined ODQ-QBPD-BPFS items were arranged according to difficulty. For all subjects, the most difficult item was 'pain intensity', whereas the easiest was 'take food out of the refrigerator'. As the separation index of 42 combined ODQ-QBPD-BPFS was higher than that of the three questionnaires separately, difficulty of items varied with some need for rearrangement. The results of this study confirmed the possibility and need for a new back pain disability assessment tool, and produced one. Further study is needed to refine the questionnaire in consideration of psychosocial and occupational factors.

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)0.000
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 score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.337
Teacher spread0.292 · 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
Published2011
Admission routes1
Has abstractyes

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