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Record W2990594306 · doi:10.1097/prs.0000000000006268

Contemporary Approaches to Postoperative Pain Management

2019· article· en· W2990594306 on OpenAlexaff
Amanda Murphy, Siba Haykal, Donald H. Lalonde, Toni Zhong

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsSaint John Regional HospitalCanadian Institutes of Health ResearchUniversity of TorontoUniversity of New BrunswickCanadian Cancer SocietyUniversity Health NetworkQuebec Breast Cancer FoundationToronto Public Health
Fundersnot available
KeywordsPostoperative painMultimodal therapyCornerstoneMedicinePain managementRegimenReading (process)OpioidOpioid epidemicPhysical therapyAnesthesiaSurgery

Abstract

fetched live from OpenAlex

LEARNING OBJECTIVES: After reading this article, the participant should be able to: 1. Describe the fundamental concepts of multimodal analgesia techniques and how they target pain pathophysiology. 2. Effectively educate patients on postoperative pain and safe opioid use. 3. Develop and implement a multimodal postoperative analgesia regimen. SUMMARY: For many years, opioids were the cornerstone of postoperative pain control, contributing to what has become a significant public health concern. This article discusses contemporary approaches to multimodal, opioid-sparing postoperative pain management in the plastic surgical patient.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.065
GPT teacher head0.227
Teacher spread0.162 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2019
Admission routes1
Has abstractyes

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