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Record W4308056709 · doi:10.1111/anae.15873

Network meta‐analysis of the analgesic effectiveness of regional anaesthesia techniques for anterior cruciate ligament reconstruction

2022· review· en· W4308056709 on OpenAlexaff
Nasir Hussain, Richard Brull, Christopher Vannabouathong, Jarod Speer, Christopher M. Lagnese, Colin J. L. McCartney, Faraj W. Abdallah

Bibliographic record

VenueAnaesthesia · 2022
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsSt. Michael's HospitalOttawa HospitalUniversity of OttawaWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineAnalgesicRegional anaesthesiaAnterior cruciate ligamentAnesthesiaAnterior cruciate ligament reconstructionMeta-analysisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Anterior cruciate ligament reconstruction can cause moderate to severe acute postoperative pain. Despite advances in our understanding of knee innervation, consensus regarding the most effective regional anaesthesia techniques for this surgical population is lacking. This network meta-analysis compared effectiveness of regional anaesthesia techniques used to provide analgesia for anterior cruciate ligament reconstruction. Randomised trials examining regional anaesthesia techniques for analgesia following anterior cruciate ligament reconstruction were sought. The primary outcome was opioid consumption during the first 24 h postoperatively. Secondary outcomes were: rest pain at 0, 6, 12 and 24 h; area under the curve of pain over 24 h; and opioid-related adverse effects and functional recovery. Network meta-analysis was conducted using a frequentist approach. A total of 57 trials (4069 patients) investigating femoral nerve block, sciatic nerve block, adductor canal block, local anaesthetic infiltration, graft-donor site infiltration and systemic analgesia alone (control) were included. For opioid consumption, all regional anaesthesia techniques were superior to systemic analgesia alone, but differences between regional techniques were not significant. Single-injection femoral nerve block combined with sciatic nerve block had the highest p value probability for reducing postoperative opioid consumption and area under the curve for pain severity over 24 h (78% and 90%, respectively). Continuous femoral nerve block had the highest probability (87%) of reducing opioid-related adverse effects, while local infiltration analgesia had the highest probability (88%) of optimising functional recovery. In contrast, systemic analgesia, local infiltration analgesia and adductor canal block were each poor performers across all analgesic outcomes. Regional anaesthesia techniques that target both the femoral and sciatic nerve distributions, namely a combination of single-injection nerve blocks, provide the most consistent analgesic benefits for anterior cruciate ligament reconstruction compared with all other techniques but will most likely impair postoperative function. Importantly, adductor canal block, local infiltration analgesia and systemic analgesia alone each perform poorly for acute pain management following anterior cruciate ligament reconstruction.

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)0.015
metaresearch head score (Gemma)0.037
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: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.044
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.076
GPT teacher head0.345
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 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

Citations21
Published2022
Admission routes1
Has abstractyes

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