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Record W3165664131 · doi:10.6215/fjpt.2018.75.p43

【論文摘要】系統性回顧文獻:疼痛神經生理學衛教合併運動治療對於慢性下背痛的疼痛與生活功能影響

2018· article· zh· W3165664131 on OpenAlexaboutno aff
謝美琪, 楊輝宏, 甯智航

Bibliographic record

Venue物理治療 · 2018
Typearticle
Languagezh
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsOswestry Disability IndexVisual analogue scaleLow back painMedicineBack painPhysical therapyRating scalePsychologyAlternative medicinePathologyDevelopmental psychology

Abstract

fetched live from OpenAlex

背景與目的:臨床上常見個案因為疼痛的關係發展出懼怕活動的現象,在運動介入或者讓個案返回生活上會遇到困難。疼痛神經生理學衛教(pain neurophysiology education, PNE)會介紹疼痛知覺系統的變化,對於長期疼痛衍伸出的神經系統敏感化進而發展出的懼怕活動現象可能會有幫忙,故本篇想利用系統性文獻回顧的方式來探討PNE是否能改善慢性下背痛疾患的疼痛和生活功能。方法:以chronic low back pain和pain neurophysiology education為關鍵字使用PubMed電子文獻資料庫搜索至2017年12月所發表的文獻。結果:經搜尋共有十篇文獻,人工篩選後符合主題文章僅有四篇。其中比較PNE與傳統衛教組(未提到治療性運動介入)有兩篇,比較PNE合併運動治療與單獨運動治療有兩篇。疼痛的部分,2004年Moseley學者等人發現PNE相較傳統衛教(PEDro: 7/10)在疼痛態度量表(修訂版)(brief Survey of Pain Attitudes [revised])的效應值(95%信賴區間)為9 (6.5 ~ 11.5),有達統計上的差異;2014年Louw學者等人(PEDro: 7/10)發現三個月後兩組的疼痛(numeric pain rating scale)沒有統計上的差異;2015年Pires學者等人(PEDro: 8/10)發現PNE合併運動治療相較單獨運動治療,在3個月後疼痛(Visual Analogue Scale)顯著較低(p < 0.005),2017年Gema學者等人(PEDro: 8/10)發現疼痛(Numeric Pain Rating Scale)顯著較低(p < 0.001)。在功能的部分,Moseley學者等人發現PNE相較傳統衛教,在羅蘭—摩理斯生活障礙問卷(Roland Morris Disability Questionnaire)的效應值(95%信賴區間)為2(0.4 ~ 3.6),在統計上有顯著差異(p = 0.02),但未達到臨床最小重要差異值(minimal clinically important difference, MCID),Louw學者等人發現在歐氏失能量表(Oswestry Disability Index)兩組沒有統計上的差異(p > 0.167);Pires學者等人發現PNE合併運動治療相較單獨運動治療,在魁北克背痛生活障礙量表(Quebec Back Pain Disability Scale)沒有組間統計上的差異,Gema學者等人發現在羅蘭─摩理斯生活障礙問卷功能顯著較佳(p < 0.001)。結論:慢性下背痛患者若只提供疼痛神經生理學衛教,沒有治療性運動差異不明顯。疼痛神經生理學衛教合併運動治療在疼痛是有效益的,在功能上未有一致的結果。這樣的結果可能與運動的類型有關,未來若能有更多的相關研究,可能會有進一步的瞭解。臨床意義:慢性下背痛的物理治療介入,若能加入疼痛神經生理學衛教內容,有機會更有效率的降低下背痛疾患的疼痛與提升日常生活功能。

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.005
Scholarly communication0.0050.008
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.013
GPT teacher head0.302
Teacher spread0.289 · 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 designSystematic review
Domainnot available
GenreReview

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".

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Published2018
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