Servikal ve lumbar ağrı problemi olan hastaların ağrı, emosyonel durum ve yaşam kalitelerinin karşılaştırılması
Bibliographic record
Abstract
Purpose: The purpose of this study was to compare the patients with cervical and lumbar region pain problems for pain, emotional status and quality of life. Methods: Six hundred voluntary patients aged between 20-65 years (lumbar group: 43.2±11 years, neck group: 42,8±10,2 years), 300 of the patients with cervical pain and 300 patients with low back pain problems participated in the study. We evaluated the degree and nature of pain by Short Form McGill Pain Questionnaire (SF-MPQ), the emotional status by Hospital Anxiety-Depression Measure (HAD) and the quality of life by Nottingham Health Profile (NHP). Results: There was no difference between pain scores of the patients (lumbar group: 6.7±2, neck group: 6.8±2) (p>0.05). Comparisons showed that there was no statistically significant relationship between the groups for HAD scores (HADanxiety lumbar group 7.92±3.99 cervical group 8.02±4, HADdepression lumbar group 6.46±3.68 cervical group 6.54±3.65) (p>0.05), but there was significant difference for pain and physical activity parameters of NHP scores for back pain group (NHP pain; lumbar group: 54.85±26.16, neck group: 45.13±29.47), (NHP physical activity; lumbar group: 33.35±16.18, neck group: 25.65±17.51) (p<0.05). Conclusion: The result of the study showed that the quality of life for low back pain group was more affected than neck pain group.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".