Bibliographic record
Abstract
目的 观察天灸联合康复训练治疗腰肌筋膜疼痛综合征的疗效.方法 共选取60例腰肌筋膜疼痛综合征患者,将其随机分为治疗组及对照组,治疗组给予天灸治疗及康复训练,对照组给予假天灸治疗及康复训练.于治疗1 d及3周时采用简化McGill疼痛量表对患者进行评定,观察2组患者治疗期间副反应发生情况;于治疗后3个月时进行随访,观察2组患者腰痛复发情况.结果 治疗1 d时,治疗组患者疼痛分级指数(PRI)、疼痛目测类比评分(VAS)及现有疼痛强度(PPI)均较治疗前显著改善(P 0.05),组问差异具有统计学意义(P 0.05).在治疗期间,发现治疗组有少许患者诉皮肤瘙痒、水疱、皮肤灼痛等副反应,经处理后均自行消退.治疗后3个月时随访发现,治疗组患者腰痛复发情况明显优于对照组(P<0.05),2组患者随访期问均无疤痕、感染发生.结论 天灸联合康复训练治疗腰肌筋膜疼痛综合征即时疗效及远期疗效显著,并且还具有治疗方便、副反应少、痛苦小、患者依从性好等优点,值得临床推广、应用。
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".