Efficacy of Daiwenjiu ointment on the treatment of cervical spondylosis with nerve root type caused by cold dampness obstruction
Bibliographic record
Abstract
Objective This study aimed to assess the clinical efficacy of Daiwenjiu ointment in the treatment of cervical spondylosis with cold dampness obstruction nerve root type.Methods A retrospective analysis was conducted on a cohort of 110 patients diagnosed with cervical spondylotic radiculopathy. Based on the treatment method, the patients were divided into two groups. The control group received electroacupuncture treatment, while the observation group received a combination of Daiwenjiu ointment and electroacupuncture treatment. The outcome measures included Japanese Orthopedic Association (JOA) scores for cervical spine function, Simplified McGill Pain Questionnaire (SF-MPQ) scores, and changes in serum inflammatory factors TNF-α and IL-1β.Results Following treatment, the JOA score in the observation group increased from 9.45 ± 1.35 to 14.82 ± 1.29 after treatment, indicating better recovery of cervical spine function compared to the control group (p < 0.001). The SF-MPQ score in the observation group decreased to 18.25 ± 3.80 after treatment, while it remained at 30.20 ± 4.30 in the control group. This difference between the groups was statistically significant (p < 0.001). Furthermore, the observation group demonstrated a significant decrease in serum levels of TNF-α and IL-1β after treatment compared to the control group (p < 0.001).Conclusion Daiwenjiu ointment exhibits significant therapeutic effects in patients with cold dampness obstruction nerve root type cervical spondylosis. It effectively improves cervical function, reduces pain, and downregulates inflammatory cytokine levels.
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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) | 0.001 | 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) | 0.000 | 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".