A fájdalom intenzitását mérő skálák összehasonlítása ágyéki gerincfájdalommal élők körében
Bibliographic record
Abstract
Background and purpose: Pain intensity is the most frequently assessed health domain in clinical studies among patients with low-back pain. Visual analogue scale (VAS) and Numeric rating scale (NRS) have been the mostly used measurement tools for pain intensity. We proposed to correlate these instruments to a generic health-related quality of life measurement tool in order to show the scale with superior clinical relevance. . Methods: We used cross-sectional, convenience sampling. 120 patients with chronic low-back pain administered the 29-item Patient Reported Outcomes Measurement Information System Profile with NRS included, and the VAS scale in the National Institute of Mental Health, Neurology and Neurosurgery. We determined the correlation between PROMIS domain T-scores and VAS and NRS scores. . Results: We performed Spearman rank correlation test to calculate the correlation coefficient. We found VAS scales measuring pain had weak to moderate correlations with all PROMIS health domains (r = 0.24–0.55). Therefore, we compared correlation of PROMIS domain scores with PROMIS pain intensity numeric rating scale and VAS scales. PROMIS domains had moderate to strong correlations with pain intensity scale (r = 0.45–0.71). PROMIS physical function short form [r = –0.65, 95% CI (–0.75) – (–0.55)] and PROMIS pain interference short form (r = 0.71, 95% CI 0.63 – 0.79) had the strongest correlation with pain intensity item. . Conclusion: NRS has showed greater correlation with PROMIS domain T-scores than VAS scale. This may prove that NRS has greater connection to another health domains, thus it correlated more to health-related quality of life than visual scale. We recommend NRS to use in further clinical studies conducted among patients with 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".