Transforaminal versus Intra-Articular Facet Corticosteroid Injections for the Treatment of Cervical Radiculopathy: A Randomized, Double-Blind, Controlled Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Transforaminal corticosteroid injections can be performed in the management of cervical radiculopathy but carry the risk of catastrophic complications. This study compares the efficacy of transforaminal and facet corticosteroid injections at 4 weeks' follow-up. MATERIALS AND METHODS: We randomly assigned 56 subjects to receive CT-guided transforaminal (15 men, 13 women; mean age, 52 years; range, 28-72 years) or facet (8 men, 20 women; mean, 44 years; range, 26-60 years) injections. The primary outcome was pain severity rated on a Visual Analog Scale (0-100). Secondary outcomes were the Neck Disability Index and the Medication Quantitative Scale. RESULTS: In the intention-to-treat and as-treated analyses, for a mean baseline score, facet injections demonstrated a significant pain score reduction of 45.3% (95% CI, 21.4-69.2) and 37.0% (95% CI, 9.2-64.7), while transforaminal injections showed a nonsignificant pain score reduction of 9.8% (95% CI, +11.5-31.2) and 17.8% (95% CI, +6.6-42.2). While facet injections demonstrated an improvement in the Neck Disability Index score of 24.3% (95% CI, +2.9-51.5) and 20.7% (95% CI, +6.2-47.6) as opposed to transforaminal injections of 9.6% (95% CI, +15.2-34.4) and 12.8% (95% CI, +11.2-36.7), the results were not statistically significant. Noninferiority of facet to transforaminal injections was demonstrated for baseline pain scores of ≤60, while noninferiority analysis was inconclusive for baseline pain scores of ≥80 and for the Neck Disability Index. Neither intervention showed a significant medication-intake score reduction with time. CONCLUSIONS: Facet injections are effective for the treatment of cervical radiculopathy and represent a valid and safer alternative to transforaminal injections.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".