MétaCan
Menu
Back to cohort
Record W3210952712 · doi:10.3233/bmr-191824

Evaluation of the correlation between the Istanbul Low Back Pain Disability Index, Back Pain Functional Scale and other back pain disability scales in Turkish patients with low back pain

2021· article· en· W3210952712 on OpenAlexaboutno aff
Ahmet Karadağ, Muhammed Canbaş

Bibliographic record

VenueJournal of Back and Musculoskeletal Rehabilitation · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsLow back painOswestry Disability IndexMedicinePhysical therapyVisual analogue scaleBack painCorrelationPhysical medicine and rehabilitationAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Low back pain is an important health problem that may cause functional loss. Several back pain disability scales have been developed in different languages. OBJECTIVE: The present study evaluates the correlation between the Istanbul Low Back Pain Disability Index (ILBPDI) the Back Pain Functional Scale (BPFS) and other back pain disability scales in patients with mechanical low back pain. METHODS: Included in the study were 105 patients who presented to our outpatient clinics and who were diagnosed with mechanical low back pain. The ILBPDI, BPFS, Quebec back pain disability scale (QBPDS) and Oswestry low back pain disability questionnaire (ODI) were administered to all participants, and Visual analogue scale (VAS) scores were recorded. RESULTS: A strongly negative correlation was identified between ILBPDI and BPFS (p< 0.05), and a strongly positive correlation was noted between ILBPDI and QBPDS, ODI and VAS. CONCLUSION: A strong correlation exists between ILBPDI and BPFS, and a further strong correlation between ILBPDI ODI and QBPDS. These questionnaires can be used interchangeably to evaluate disability associated with chronic mechanical low back pain.

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)0.037
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0370.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.260
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Back and Musculoskeletal RehabilitationSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207