Particulate and Non-Particulate Steroids in Lumbar Transforaminal Epidural Injections
Bibliographic record
Abstract
Dear Editor, We welcome the efforts of El-Yahchouchi et al. [1] on undertaking a retrospective study of a large patient cohort to address the important issue of comparison of efficacy of particulate (triamcinolone and betamethasone) and nonparticulate (dexamethasone) lumbar transforaminal epidural steroid injections (TFESI). The authors conclude that lumbar TFESI of dexamethasone is noninferior to betamethasone and triamcinolone for treatment of radicular pain and that dexamethasone is superior to triamcinolone and betamethasone with regard to pain and functional outcome at 2 months. However, we have several questions regarding their methodology and conclusions of this article. First, particulate steroids were used in 87% (N = 3,162) of their TFESIs, whereas dexamethasone was used in only 13% (N = 481). This is a considerable difference in sample size between the particulate and nonparticulate groups, and it is reflected in the wider confidence intervals in the dexamethasone group. Furthermore, there was a significant difference in proportion of patients with pain less than 3 months—27.1% patients in the dexamethasone group as compared with 20.9% and 22% of patients in the two particulate steroid groups (P = 0.0175). Authors have also stated that follow-up data were available for only 81% and 60% of total TEESIs at 2 weeks and 2 months, respectively. They also fail to mention the difference in the missing data between the particulate and nonparticulate groups. We believe that difference in the group sizes and missing information could have contributed to erroneous results and conclusions through overlap with the margin for noninferiority.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".