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Record W4366449345 · doi:10.1136/bmj-2022-070730

Surgical versus non-surgical treatment for sciatica: systematic review and meta-analysis of randomised controlled trials

2023· review· en· W4366449345 on OpenAlexaff
Chang Liu, Giovanni E Ferreira, Christina Abdel Shaheed, Qiuzhe Chen, Ian A. Harris, Christopher S. Bailey, Wilco C. Peul, Bart W. Koes, Chung‐Wei Christine Lin

Bibliographic record

VenueBMJ · 2023
Typereview
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsMedicineSciaticaMeta-analysisPlaceboPhysical therapyRandomized controlled trialCINAHLAdverse effectMEDLINEClinical trialSurgeryInternal medicinePsychological interventionAlternative medicine

Abstract

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OBJECTIVE: To investigate the effectiveness and safety of surgery compared with non-surgical treatment for sciatica. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Medline, Embase, CINAHL, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and the World Health Organisation International Clinical Trials Registry Platform from database inception to June 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised controlled trials comparing any surgical treatment with non-surgical treatment, epidural steroid injections, or placebo or sham surgery, in people with sciatica of any duration due to lumbar disc herniation (diagnosed by radiological imaging). DATA EXTRACTION AND SYNTHESIS: Two independent reviewers extracted data. Leg pain and disability were the primary outcomes. Adverse events, back pain, quality of life, and satisfaction with treatment were the secondary outcomes. Pain and disability scores were converted to a scale of 0 (no pain or disability) to 100 (worst pain or disability). Data were pooled using a random effects model. Risk of bias was assessed with the Cochrane Collaboration's tool and certainty of evidence with the grading of recommendations assessment, development, and evaluation (GRADE) framework. Follow-up times were into immediate term (≤six weeks), short term (>six weeks and ≤three months), medium term (>three and <12 months), and long term (at 12 months). RESULTS: 24 trials were included, half of these investigated the effectiveness of discectomy compared with non-surgical treatment or epidural steroid injections (1711 participants). Very low to low certainty evidence showed that discectomy, compared with non-surgical treatment, reduced leg pain: the effect size was moderate at immediate term (mean difference -12.1 (95% confidence interval -23.6 to -0.5)) and short term (-11.7 (-18.6 to -4.7)), and small at medium term (-6.5 (-11.0 to -2.1)). Negligible effects were noted at long term (-2.3 (-4.5 to -0.2)). For disability, small, negligible, or no effects were found. A similar effect on leg pain was found when comparing discectomy with epidural steroid injections. For disability, a moderate effect was found at short term, but no effect was observed at medium and long term. The risk of any adverse events was similar between discectomy and non-surgical treatment (risk ratio 1.34 (95% confidence interval 0.91 to 1.98)). CONCLUSION: Very low to low certainty evidence suggests that discectomy was superior to non-surgical treatment or epidural steroid injections in reducing leg pain and disability in people with sciatica with a surgical indication, but the benefits declined over time. Discectomy might be an option for people with sciatica who feel that the rapid relief offered by discectomy outweighs the risks and costs associated with surgery. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021269997.

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)0.029
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.077
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0320.037
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.421
GPT teacher head0.521
Teacher spread0.100 · 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 designMeta-analysis
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".

Quick stats

Citations81
Published2023
Admission routes1
Has abstractyes

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