Correlation between Visual Analogue Scale and Short form of McGill Questionnaire in Patients with Chronic Low Back Pain
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".