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Record W4285732432 · doi:10.1177/11207000221111309

Quadratus lumborum block for postoperative pain management in patients undergoing total hip arthroplasty: a systematic review and meta-analysis

2022· review· en· W4285732432 on OpenAlexaff
Aaron Gazendam, Meng Zhu, Luc Rubinger, Yaping Chang, Steve Phillips, Mohit Bhandari

Bibliographic record

VenueHip International · 2022
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMeta-analysisAdverse effectArthroplastyRandomized controlled trialPerioperativeMEDLINEOpioidSubgroup analysisSystematic reviewAnesthesiaPhysical therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The use of quadratus lumborum nerve blocks (QLB) for pain control following elective total hip arthroplasty (THA) has increased substantially in recent years. The objective of this systematic review and meta-analysis was to compare outcomes from randomised controlled trials (RCTs) utilising QLBs following elective THA. METHODS: MEDLINE, EMBASE, and Cochrane databases were searched for RCTs perioperative QLBs for THA. Quantitative synthesis was conducted for pain scores, opioid consumption and adverse events. RESULTS: A total of 7 RCTs with 429 patients undergoing THA were included. No differences in pain scores were demonstrated between QLBs and control interventions. Subgroup analysis demonstrated no differences between QLBs and sham procedures or active comparators. No differences in postoperative opioid consumption between QLB and control interventions was found. In trials reporting adverse events, they were rare and similar between groups. Overall, the certainty of the evidence was graded as low or very low. CONCLUSIONS: The current literature suggests that a QLB for THA does not reduce pain or opioid consumption compared to sham or active comparators.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.325
Teacher spread0.269 · 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 teacher head, not a consensus.

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

Citations8
Published2022
Admission routes1
Has abstractyes

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