Transforaminal epidural steroid injections prevent the need for surgery in patients with sciatica secondary to lumbar disc herniation: a retrospective case series
Bibliographic record
Abstract
BACKGROUND: The median orthopedic surgery wait time in Canada is 33.7 weeks, thus alternative treatments for pathologies such as lumbar disc herniations (LDH) are needed. We sought to determine whether transforaminal epidural steroid injections (TFESIs) alleviate or merely delay the need for surgery. METHODS: We retrospectively reviewed the charts of patients with LDH who received TFESIs between September 2006 and July 2008. Patient demographics, level and side of pathology, workers' compensation status, levels injected, treatment outcome and time from referral to treatment were evaluated. The primary outcome measure was the need for versus the avoidance of surgery. RESULTS: We included 91 patients in our analysis. Time from family physician referral to injection was 123 (standard deviation [SD] 88) days; no significant differences in wait times were found between TFESI patients and those requiring surgery. In all, 51 patients (22 women, 29 men) with a mean age of 45.8 (SD 10.2) years avoided surgery following TFESI, whereas 40 patients (16 women, 24 mean) with a mean age of 43.1 (SD 12.0) years proceeded to surgery within 189 (SD 125) days postinjection. In all, 15 patients received multiple injections, and of these, 9 did not require surgical intervention. Age, sex and level/side of pathology did not influence the treatment outcome. Workers' compensation status influenced outcome significantly; these patients demonstrated less benefit from TFESI. CONCLUSION: Transforaminal epidural steroid injections are an important treatment tool, preventing the need for surgery in 56% of patients with LDH.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".