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Carbamazepine combining high frequency electroacupuncture therapy for trigeminal neuralgia and its effects on patients’ pain relief and life quality

2018· article· en· W3028848325 on OpenAlexaboutno aff
Yanhua Yin, Hai-Meng Zhou

Bibliographic record

Venue国际医药卫生导报 · 2018
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsTrigeminal neuralgiaElectroacupunctureCarbamazepineMedicineAnesthesiaQuality of life (healthcare)AcupuncturePain reliefNeuralgiaToothacheNeuropathic painEpilepsyTraditional medicineAlternative medicine

Abstract

fetched live from OpenAlex

目的 探究卡马西平(CBZ)联合高频电针治疗原发性三叉神经痛(TN)的效果及对疼痛缓解、生活质量的影响。 方法 选取本院124例TN患者为研究对象,采用随机数字表法分为CBZ联合高频电针组(观察组,n=62)和CBZ组(对照组,n=62)。记录两组治疗3个月后临床疗效差异,并比较两组治疗前及治疗28 d后睡眠质量[理查兹-坎贝尔睡眠量表(RCSQ)]、疼痛情况[简化McGill疼痛问卷表(SF-MPQ)]及生活质量[健康生活质素调查问卷(SF-12)-生理总得分(PCS)、心理总得分(MCS)]差异。 结果 观察组疗效明显优于对照组(P<0.05)。治疗28 d后,两组RCSQ、PCS、MCS评分均较治疗前升高(均P<0.05),SF-MPQ评分则较治疗前降低(均P<0.05),且观察组变化幅度均大于对照组(均P<0.05)。 结论 CBZ联合高频电针治疗TN的效果显著,能有效缓解患者疼痛,提高其睡眠质量,对改善患者生活质量也有积极意义。

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.277
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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