Tizanidine is effective in the treatment of myofascial pain syndrome.
Bibliographic record
Abstract
Myofascial pain syndrome (MPS) is difficult to treat. The efficacy and safety of tizanidine, an alpha2-adrenergic agent with effects on spasticity and pain, in treating MPS was evaluated. Female subjects (n = 29) with MPS of 9 to > 52 weeks' duration and mean age 37.5 (range 20-51) years, who also had reduced pressure thresholds, were enrolled. Subjects were titrated up to 12 mg of tizanidine over 3 weeks and maintained for 2 weeks. Sleep was assessed via visual analog scale (VAS), pain intensity via short form McGill questionnaire including VAS, disability/level of function, and pressure threshold (tested by algometry) at baseline, weeks 3 and 5, and 1 week after tizanidine was discontinued. Patient and physician global assessments of treatment were reported at week 5. Twenty-four subjects completed the study. Pain intensity and disability decreased significantly from baseline at weeks 3 and 5 and after washout (P < .001). Pressure threshold and sleep improved for all study periods (P < .001). Tizanidine was rated as good to excellent in relieving pain by 89% of subjects and 79% of physicians. No serious adverse events occurred. Tizanidine was effective in the treatment of MPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".