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Record W4288517315 · doi:10.1097/aco.0000000000001169

Regional analgesia for cardiac surgery

2022· review· en· W4288517315 on OpenAlexaff
Michael C. Grant, Alexander J. Gregory, Jean-Pierre P. Ouanes

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2022
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePerioperativeVariety (cybernetics)Intensive care medicineCardiac surgeryOpioidPain managementRegional anaesthesiaRegional anesthesiaAnesthesiaSurgeryComputer science

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Regional anesthesia is gaining attention as a valuable component of multimodal, opioid-sparing analgesia in cardiac surgery, where improving the patient's quality of recovery while minimizing the harms of opioid administration are key points of emphasis in perioperative care. This review serves as an outline of recent advancements in a variety of applications of regional analgesia for cardiac surgery. RECENT FINDINGS: Growing interest in regional analgesia, particularly the use of newer "chest wall blocks", has led to accumulating evidence for the efficacy of multiple regional techniques in cardiac surgery. These include a variety of technical approaches, with results consistently demonstrating optimized pain control and reduced opioid requirements. Regional and pain management experts have worked to derive consensus around nerve block nomenclature, which will be foundational to establish best practice, design and report future research consistently, improve medical education, and generally advance our knowledge in this vital area of perioperative patient care. SUMMARY: The field of regional analgesia for cardiac surgery has matured over the last several years. A variety of regional techniques have been described and shown to be efficacious as part of the multimodal, opioid-sparing approach to pain management in the cardiac surgical setting.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.229
GPT teacher head0.409
Teacher spread0.180 · 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 designNot applicable
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

Citations14
Published2022
Admission routes1
Has abstractyes

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