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Postoperative Pain in Patients Undergoing Urologic Endoscopy

2005· article· en· W3030733232 on OpenAlexaboutno aff
董庆龙, 庄小雪, 欧阳葆怡, 李逊

Bibliographic record

VenueXiandai linchuang yixue shengwu gongchengxue zazhi · 2005
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEndoscopyGeneral surgerySurgery

Abstract

fetched live from OpenAlex

目的 观察经尿道前列腺切除术(TURP)、经尿道输尿管取石术(URL)和微创经皮肾穿刺取石术(MPCNL)患者术后疼痛程度与规律.方法随机选择一期择期手术成年患者分为4组,Ⅰ组:ⅠLU组为左URL(52例),ⅠRU组右URL(57例),ⅠDU组双侧URL(15例);Ⅱ组:ⅡLP组左MPCNL(66例),ⅡRP组右MPCNL(63例);Ⅲ组TURP(59例);Ⅳ组:ⅣT组TURP(30例),ⅣP 组MPCNL(34例),两亚组均术毕静注氯诺昔康8 mg,继以术后静脉自控镇痛治疗(PCIA) (0.04%,2 ml/h).以视觉模拟评分法(VAS)测评术后24h内最大疼痛程度(Pmax)、术后第24h时点的疼痛程度(P24)和McGill疼痛问卷(MPQ)查询.结果Ⅱ组各亚组与Ⅲ组、Ⅱ组各亚组与Ⅰ组各亚组的Pmax或P24组间比较均有统计学差异(p<0.05);ⅣT的Pmax或P24均小于Ⅲ组(p<0.05),ⅣP的Pmax或P24均小于ⅡLP或ⅡRP(p<0.05),Ⅰ~Ⅲ组术后24h需应用镇痛药者分别为12例(9.7%)、27例(20.9%)和4例(7.3%),各组中应用与无应用镇痛药患者的Pmax或P24组间比较均有显著统计学差异(p<0.05),Ⅰ和Ⅲ组为会阴部胀痛,Ⅱ组为术侧腰部钝痛与胀痛.结论 TURP和MPCNL患者术后应积极进行疼痛治疗,氯诺昔康能有效地缓解术后疼痛。

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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 designObservational
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

Citations0
Published2005
Admission routes1
Has abstractyes

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