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Record W7065086095

Correlation between Visual Analogue Scale and Short form of McGill Questionnaire in Patients with Chronic Low Back Pain

2012· article· en· W7065086095 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsVisual analogue scaleMcGill Pain QuestionnaireCorrelationIntensity (physics)Low back painChronic painPearson product-moment correlation coefficient
DOInot available

Abstract

fetched live from OpenAlex

Background and Objectives: Pain assessment in patients with chronic pain is very important. This study was performed to evaluate the pain severity and the correlation between visual analogue scales (VAS) and short form of McGill questionnaires in patients with chronic low back pain.Methods: In a prospective descriptive study, pain intensity in 150 patients who were referred to the Physiotherapy Ward of Baghiatallah Hospital in Tehran, was measured by Mc-gill and VAS pain questionnaires, and then the correlation between these questionnaires was evaluated. Data was analyzed by Pearson correlation and regression statistical tests.Results: The patients’ pain intensity score was 8.36±0.9 by VAS and 39.04±4.2 By Mc-gill questionnaire, respectively. The data show that most of our samples had severe pain. The correlation between VAS and Mc-gill was r=0.86 that shows a very good correlation between these questionnaires. VAS as this formula predicts McGill: McGill=4.727+4.1(VAS), R²=0.771.Conclusion: Regarding the importance of pain evaluation and excellent correlation between VAS and Mc-gill, and the fact that VAS questionnaire is very easy to be completed, it seems that VAS questionnaire is superior to short form to McGill pain questionnaire to evaluate pain in patients with chronic 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.437
Teacher spread0.388 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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