Association between Severity of Low Back Pain (LBP) and Magnetic Resonance Imaging Pathologies in Patients with Acute LBP
Bibliographic record
Abstract
Background and Objectives: Low Back Pain (LBP) is a common health problem that affects people worldwide, and about 15% of Iranians. It imposes high costs to societies and requires great attention. Magnetic Resonance Imaging (MRI) is considered a reliable and accurate diagnostic tool, however some studies have questioned the appropriateness of MRI in LBP. Thus, this study aimed to evaluate the relationship between the intensity of LBP and positive findings in MRI, in patients with acute LBP. Materials and Methods: A cross-sectional study was performed on all patients, who reported less than four weeks of LBP and were referred to the radiology center of Shafa-e-Yahyaeian hospital, ranged from March to July, 2014, to perform MRI. Data collected included demographics and pain characteristics, in addition to an Oswestry Disability Index questionnaire. MRI pathologies were distinguished by two spinal surgeons and one radiologist. Data was analyzed using the SPSS software. Results: A total of 200 patients were enrolled in this study with a mean age of 41.78 years. Nearly half (48%) of the studied patients had positive MRI findings on L4-L5, and NFS and disc bulging (42.5%, and 38%, respectively) were the most common pathologies, while 11.5% of patients had normal MRIs. The mean ODI was highest in patients with vertebral body fracture, and disc herniation and lowest in hemangioma. Conclusion: A noticeable percentage of patients demonstrated a high ODI score, but MRI findings were in significant association with age of patients, indicating that patients with acute LBP do not require MRI immediately. Using the ODI questionnaire can help us towards a better diagnostic approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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) | 0.000 | 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 teacher head, 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".