[Efficacy of tramadol/acetaminophen medication for central post-stroke pain].
Bibliographic record
Abstract
OBJECTIVE: Central post-stroke pain(CPSP)is the most difficult type of central neuropathic pain to control with medical treatment. Opioids are commonly used for chronic neuropathic pain, but their efficacy in treating central neuropathic pain, particularly CPSP, is not clear. Tramadol is an opioid analgesic that, in combination with acetaminophen, has been approved since 2011 for the treatment of non-cancer pain in Japan. In this study we evaluated the efficacy of tramadol/acetaminophen medication for CPSP. METHODS: We retrospectively reviewed nine cases of CPSP that received oral tramadol/acetaminophen medication. All cases received tramadol/acetaminophen medication after first taking pregabalin then antidepressant medication. Pain levels were assessed before tramadol/acetaminophen medication began and one month after a maintenance dose was reached, using a visual analogue scale(VAS)and the McGill pain questionnaire(MPQ). RESULTS: The mean dose of tramadol was 121±61.6 mg/day. Tramadol/acetaminophen medication was effective in reducing pain in seven of nine cases(77.8%). The VAS improved 32.9±13.8% from pre-to post-medication, and the MPQ improved from 15.4±9.1 pre-medication to 8.1±4.7 post-medication(p<0.05). These effects continued 9.3±4.5 months during follow up periods. Side effects were observed in six cases(one severe, one moderate, two mild, two transient), but medication was continued in eight cases. CONCLUSION: Oral tramadol/acetaminophen medication was effective at reducing pain levels in patients with CPSP, and is a medication option for the treatment of CPSP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".