Clinical observation on analgesic effect of abdominal acupuncture combined with McKenzie therapy on cervical spondylosis of neck type
Bibliographic record
Abstract
Objective:To observe the improvement of neck pain treated by abdominal acupuncture, McKenzie therapy and combination with each other, and provide evidence for clinical treatment on cervical spondylosis of neck type. Method:According to inclusion and exclusion criteria, 90 patients were randomized to A, B and C groups accepted the treatments of abdominal acupuncture, McKenzie therapy and abdominal acupuncture combined with McKenzie therapy respectively. Analgesic effect and the change of McGill scores [including pain rating index (PRI), visual analogue scale(VAS) and present pain intensity(PPI)] were observed. Result:The overall response rates of analgesic effect were 63.3%, 96.7% and 100% respectively with significant differences among three groups(χ2 =35.596, P0.001). All 3 therapies could obviously decrease the scores of neck pain, and inter- groups comparison analyses revealed significant differences (F=19.452, P0.001). The McGill scores were obviously significant different in aspects of PRI, VAS and PPI among three groups (P0.001). Intergroups comparison analyses revealed that there was no significant difference in all aspects of reduction after abdominal acupuncture and abdominal acupuncture combined with McKenzie therapy(P0.05), but both were obviously more than that after McKenzie therapy (P0.05). Conclusion: Both McKenzie therapy and abdominal acupuncture could reduce the pain symptoms of neck type cervical spondylosis. The combination with McKenzie therapy and abdominal acupuncture could increase overall response rates of analgesia significantly, but its' analgesic effect was better than that of abdominal acupuncture.
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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) | 0.001 | 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 | 0.000 |
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".