Quantitative Assessment of the Clinical Efficacy of Joint Needling plus Warm Needling in Treating Shoulder Periarthritis Patients
Bibliographic record
Abstract
Objective To investigate the efficacy of joint needling plus warm needling in relieving pain and improving motor function in shoulder periarthritis patients. Methods A randomized controlled trial was designed. Sixty-six shoulder periarthritis patients were randomly allocated to an observation group of 33 cases and a control group of 33 cases. The observation group received joint needling plus warm needling and the control group, conventional needling plus warm needling. The therapeutic effects on pain and motor function were evaluated using the Simplified Mcgill Pain Questionnaire (MPQ) and the Shoulder Periarthritis Rehabilitation and physiotherapy Function Assessment respectively. Results There was no statistically significant difference in the total efficacy rate between the two groups (P0.05). There were statistically significant differences in Pain Rating Index-Affective (PRI-A) subscore and Pain Rating Index-Total (PRI-T) score (P0.05) and no statistically significant differences in Pain Rating Index-Sensory (PRI-S) subscore, Visual Analogous Scale (VAS) score and Present Pain Intensity (PPI) score (P0.05) between the two groups. There were statistically significant differences in shoulder intorsion subscore, hand-touch-back subscore, hand-touch-ear subscore and motor function score (P0.05) and no statistically significant difference in shoulder extorsion subscore (P0.05) between the two groups. Conclusion Joint needling plus warm needling is superior to conventional needling in relieving painful emotion and improving shoulder motor function.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".