Comparing the effects of transcuataneous electrical nerve stimulation and fluoxetine on central pain in patients with spinal cord injury
Bibliographic record
Abstract
Objective To compare the effects of fluoxetine and transcuataneous electrical nerve stimulation (TENS) on central pain after spinal cord injury (SCI) using a sham-controlled crossover method.Methods Ele-ven patients with central pain after SCI were randomly divided into two groups which were then subject to 2 phases of treatment.Patients in group 1 were treated by oral intake of fluoexetine for 4 weeks followed by TENS treatment for 4 weeks.Those in group 2 were treated in the reverse sequence.A fifteen day washout period was arranged between the two phases of treatment.The short-form McGill pain questionnaire (SF-MPQ) and the Beck depression inventory (BDI) were used to assess all patients pre-and post-treatment.Results SF-MPQ scores were reduced significantly after either fluoexetine or TENS treatment.After each phase of treatment there was no significant difference between the two groups.Significant improvement in terms of BDI scores was found with fluoxetin treatment in both phases of the trial,but not with TENS treatment.Conclusions Both fluoxetine and TENS can alleviate central pain after SCI,and fluoxetine can relief patients' depression at the same time. Key words: Central pain; Spinal cord injury; Fluoxetin; Transcutaneous electrical nerve stimulation
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".