Risk factors for low back pain and its relation with pain related disability and depression in a Turkish sample.
Bibliographic record
Abstract
AIM: To investigate the relation of depression and pain-related disability associated with Low Back Pain (LBP). MATERIAL AND METHODS: The Quebec Back Pain Disability Scale, Visual Analogue Scale (VAS) and Zung Depression Scale were sent to 3800 randomly select adults in Kayseri, Turkey. The demographic characteristics of the participants (Socioeconomic status, age etc) and low back pain (frequency, intensity, duration) features together with pain-related factors were investigated in responding participants. The participants who had self-reported LBP during the study period were accepted as the study group. RESULTS: 807 (37.1%) of the participants reported that they had low back pain at the time of interview. The study group had a score of 52.91+/-24.20 mm for VAS, 52.30+/-10.67 for the Zung Depression Scale and 24.53+/-17.22 for the Quebec Back Pain Disability Scale. Age, female gender, smoking ( > 20 cigarettes per day), low socioeconomical status and living in a rural habitat were found to be associated with low back pain. Depression (P= 0.017) and disability (P= 0.002) were found to be independent risk factors for VAS. CONCLUSION: Determination of the frequency and intensity of low back pain and related factors is needed for the prevention and management of pain. Mood disorders and self reported restriction in daily activities should be screened in 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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".