Incidence of Recurrent Low Back Pain as a Side Effect of Decompressive Surgery for Lumbar Spinal Stenosis in Obese Versus Non-Obese Patients
Bibliographic record
Abstract
Studies have reported an increased incidence of recurrent post-decompression-associated lower back pain (LBP) among obese patients after Lumbar spinal stenosis (LSS) surgery. Higher prevalence of lower back pain (LBP) associated with post-decompression surgical treatment among obese or overweight female patients compared to male patients. The current study has aimed to examine the relationship between body composition and long-duration consequences of post-spinal decompression among the Saudi population. This retrospective, longitudinal study was conducted at Taif Hospital, Kingdom of Saudi Arabia (KSA), throughout ____2010-till 2015 ____. Chronic pain grade questionnaire for assessing lower back pain and any disability among post-decompression participants. The chi-square test was used to analyze independent variables, and an independent t-test was employed to detect variances between mobility, age, education, body composition, and emotional disorders. The adjustment of age, education, mobility, emotional disorder, and BMI was examined through multivariate analysis. Highly a statistically substantial difference between obese and non-obese with regard to age, emotional distress, low mobility, Body mass index (BMI), mean estimated flow of blood (p-value <0.000), and hospitalization (p-value <0.002). The results showed a statistically substantial relationship between the degree of pain and disability with patient weight (p-value: 0.05), body mass index (p-value: 0.03), and Fat mass/fat-free mass ratio (p-value: 0.05). Clinical improvement is observed in obese patients post decompression surgical intervention, but the percentage of improvement was significantly higher among the male gender compared to female obese patients.
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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 | 0.001 | 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".