Neuromodulation at spinal dorsal root entry zone in the treatment of patients with neurogenic pain
Bibliographic record
Abstract
Objective To investigate the efficacy and safety of neuromodulation at spinal dorsal root entry zone (DREZ) in the treatment of patients with neurogenic pain. Methods Sixty patients with neurogenic pain were randomly divided into two groups from January 2015 to February 2016 in Kiang Wu Hospital. Patients received spinal nerve root pulsed radiofrequency treatment in group I. Electrode temperature was set at 42 ℃, pulse interval 40 ms, 120 seconds each treatment cycle for 2 cycles. Patients received neuromodulation at spinal dorsal root entry zone in group Ⅱ, in which the patients were divide into two subgroups, group Ⅱa and Ⅱb, fifteen patients in each group. Fifteen patients were treated with soft electrode in group Ⅱa, wave length 150-500 μs, frequency 40-360 Hz, electric voltage 2-8 V, continuously stimulating for 3 days. Other fifteen patients were treated with hard electrode in group Ⅱb, the parameter setting was the same as that in group I. VAS and short-form McGill pain questionnaire (SF-MPQ) were recorded before the treatment and at 1, 4, 12 weeks after the treatment. Results Compared with pre-treatment, VAS was significantly decreased ateach time point in group I and Ⅱ after the treatment (P 0.05). Compared with group Ⅱb, PRI-S and PRI-A were significantly decreased at 12 weeks after the treatment in group Ⅱa (P<0.05). Conclusion Neuromodulation is safe and effective at spinal dorsal root entry zone in the treatment of patients with neurogenic pain. Key words: Neuralgia; Dorsal root entry zone; Neural regulation; Pulsed radiofrequency treatment
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".