Ameliorative effects of midnight-noon ebb-flow of acupoint application for patients with knee osteoarthritis pain
Bibliographic record
Abstract
Objective To explore the ameliorative effects of midnight-noon ebb-flow of acupoint application for patients with knee osteoarthritis(KOA) pain.Methods 60 patients were randomly divided into the experimental group and the control group with 30 patients in each group.The control group used acupoint patch on the affected area,and the experimental group used midnight-noon ebb-flow of acupoint application.Condensed McGill pain scale(SF-MPQ),the effect of obstacles and simplified Chinese version of the Oswestry Index Questionnaire (ODI) were used to evaluate patients' pain and dysfunction.Results The SFMPQ scores of the experimental group 2,4,8 weeks after intervention were lower than those of the control group.The comparison within groups showed that the scores 2,4,8 weeks after intervention were significantly lower than those before intervention.Further comparison showed that the scores of the latter timepoint were lower than those of the former timepoint.Conclusions The application of midnight-noon ebbflow of acupoint can effectively control joint pain of KOA patients,improve dysfunction of patients.Its effect is better than acupoint patch on the affected area,and the effects increased with the increase of time. Key words: Knee osteoarthritis; Pain; Functional disorder; Acupoint application; Midnightnoon ebb-flow
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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) | 0.000 | 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".