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Factors to consider for fascial plane blocks' success in acute and chronic pain management

2024· article· en· W4390695006 on OpenAlexaff
Pierfrancesco Fusco, Giuseppe Pascarella, Carla Stecco, Rafael Blanco, Mauricio Forero, Amit Pawa, Serkan Tulgar, Alessandro Strumia, Luigi Maria Remore, Alessandro De Cassai, Laura Brigitta Colantonio, Romualdo Del Buono, Fabrizio Fattorini, Giuseppe Sepolvere, Mario Tedesco, Gian Marco Petroni, Walter CIASCHI, Massimiliano CRASSITI, Fábio Costa

Bibliographic record

VenueMinerva Anestesiologica · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyofascial pain diagnosis and treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineModalitiesFasciaOutcome (game theory)Operator (biology)BreathingPain managementPerspective (graphical)Physical therapyIntensive care medicineSurgeryAnesthesiaArtificial intelligence

Abstract

fetched live from OpenAlex

The outcome of fascial plane blocks (FPBs) has a certain variability that may depend on many factors, which can be divided into three main categories: operator-related, patient-related and drug-related. Operator-related factors include personal skills, choice of needle and injection modalities. Patient variables include anthropometric features, the type of targeted fascia, anatomical variants, patient positioning, muscle tone and breathing. Ultimately, efficacy, onset, and duration of fascial blocks may be affected by characteristics of the injected solution, including the type of local anesthetic, volume, concentration, pH, temperature and the use of adjuvants. In this article, we investigated all the factors that may influence the outcome of FPBs from a generic perspective, without focusing on any specific technique. Also, we provided suggestions to optimize techniques for everyday practitioners and insights to researchers for future studies.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.021
GPT teacher head0.285
Teacher spread0.264 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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

Citations58
Published2024
Admission routes1
Has abstractyes

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