Frequency of vitamin d deficiency in patients with lumbar spinal stenosis and its relationship with obesity, depression, and pain intensity: a cross-sectional study
Bibliographic record
Abstract
ABSTRACT Objective This study was conducted to determine the frequency of vitamin D deficiency in patients with lumbar spinal stenosis and to define the relationship between vitamin D levels and obesity, depression, and pain intensity. Methods This study was conducted with 69 patients (Male = 32, Female = 37) diagnosed with lumbar spinal stenosis. The participants’ 25(OH)D levels were measured by radioimmunoassay. In addition, bone metabolic status, including bone mineral density and bone turnover markers, was also evaluated. The Beck Depression Inventory was used to determine the depression statuses of the patients, while the McGill Melzack Pain Questionnaire was administered to measure pain intensity. The results were evaluated at a significance level of p<0.05. Results Vitamin D deficiency (<20 ng/mL) was found in 76.8% of the patients. Binary logistic regression analysis showed a significantly higher frequency of vitamin D deficiency in patients who: 1) had higher body mass indexes (OR 3.197, 95% CI 1.549-6.599); 2) fared higher in Beck’s depression score (OR 1.817, 95% CI 1.027–3.217); and 3) were female rather than male (OR 1.700, 95% CI 0.931-3.224) (p<0.05). Conclusion In this study, vitamin D deficiency was prevalent in lumbar spinal stenosis patients. In addition, obese, depressed, and female individuals have higher risks of vitamin D deficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".