Can Quantitative Sensory Testing Predict the Outcome of Epidural Steroid Injections in Sciatica? A Preliminary Study
Bibliographic record
Abstract
Quantitative Sensory Testing (QST) is a psycho-physiological test used to identify dysfunction of individual nerve fiber types. In the present study, we investigated whether selective nerve fiber dysfunction, as assessed by QST, correlates with the effectiveness of epidural steroid injections (ESI) in patients with lumbar radiculopathy. Twenty patients with unilateral painful sciatica caused by disc herniation participated in this open study. Before ESI, quantitative thermal and mechanical sensory testing was conducted at the most painful dermatome and the contralateral dermatome. The primary outcome measure used was the self-recording of pain intensity twice daily with a 0-10 numerical pain scale (NPS). Secondary efficacy measures included the Short Form of the McGill Pain Questionnaire, the straight leg raising test, and the lumbar range of motion. A significant difference in all types of sensory thresholds between the affected and the contralateral dermatomes was detected at baseline. All outcome measures improved subsequent to the ESI. A significant positive correlation was found between the increase in cold sensation thresholds of the affected dermatome (Adelta-fiber dysfunction) and the improvement in NPS. The increase in touch and vibration thresholds (Abeta-fiber dysfunction) was found to be inversely correlated with the improvement in NPS. No correlation was found between heat sensation thresholds (C fibers) and any of the outcome measures. These results suggest that QST has the potential to be an important tool in the selection of the appropriate treatment (e.g., ESI versus surgery) for patients with sciatica and may assist in identifying the mechanisms of pain generation in these patients.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".