Bibliographic record
Abstract
目的 探讨中西医结合治疗原发性三叉神经痛的临床疗效.方法 102例原发性三叉神经痛患者按就诊顺序随机分为A、B、C三组,A组采用卡马西平治疗,B组采用中药治疗,C组采用中药联合西药治疗;根据简式McGill疼痛问卷评定三组患者治疗前后疼痛等级,同时观察三组患者治疗后的临床疗效.结果 治疗后,三组重度疼痛发生率分别为19.35％、11.76％和2.70％,三组疼痛症状均得到缓解,其中C疼痛缓解最明显,三组在疼痛等级上差异有统计学意义(P＜0.05).A组、B组和C组的临床有效率分别为64.52％、73.53％和86.49％,三组临床效果差异有统计学意义(x2=8.565,P＜0.05).结论 三种治疗方法均可缓解原发性三叉神经痛,其中中药联合西药组临床疗效最好。
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".