Comparison of the Effectiveness of Lumbar Transforaminal Epidural Injection with Particulate and Nonparticulate Corticosteroids in Lumbar Radiating Pain
Bibliographic record
Abstract
OBJECTIVE: Lumbar transforaminal epidural steroid injections are procedures often utilized in the treatment of low back pain associated with radicular pain. Particulate steroids have been known to play a role in embolism. It is, unknown whether nonparticulate steroids are as effective as particulate steroids. To investigate the effect of an epidural steroid injection on back pain, we conducted a randomized, controlled trial comparing nonparticulate steroid with particulate steroid to treat lumbar disc herniation. DESIGN: One hundred-six patients were randomized to receive lumbar transforaminal epidural steroid injections (N = 53) with either dexamethasone 7.5 mg, or with triamcinolone acetate 40 mg (N = 53). Measurement were taken before treatment and one month after treatment using a visual analog scale, short McGill pain questionnaire, and revised Oswertry Back Disability Index. RESULTS: There was a statistically significant difference in the visual analog score between those treated with dexamethasone and those given triamcinolone. The two groups did not differ significantly on the McGill Pain Questionnaire, or the Oswestry Disability Index before and after treatment. CONCLUSION: In this study, dexamethasone and triamcinolone treatments were shown to have different effects on low back pain with sciatica, with triamcinolone being more effective than dexamethsone in lumbar radiculopathy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".