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Record W3088641037 · doi:10.1055/s-0040-1717079

Nerve Block Options for Pain Control during Hepatic and Upper Abdominal Minimally Invasive Procedures

2020· article· en· W3088641037 on OpenAlexaff
Louis-Martin Boucher, Robyn Melanie Benz, Mathieu Pierotty, Rafael Melo, David Valenti

Bibliographic record

VenueDigestive Disease Interventions · 2020
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineSedationMinimally invasive proceduresPain controlSurgical proceduresSurgeryAnesthesiaIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Availability of minimally invasive abdominal procedures can be restricted in cases of painful procedures, due to limited access to anesthesia support or the more recent avoidance of aerosol generating medical procedures such as intubation. Intravenous sedation, in many cases is insufficient to effectively control the pain, leaving patients to suffer undue discomfort and a medical team stressed by the situation. Regional nerve blocks can offer a solution but are underutilized, likely in large part due to a lack of knowledge of available options. This manuscript describes four different nerve blocks that can be useful for analgesia related to upper abdominal procedures. Available data are reviewed regarding their effectiveness and the technique used to perform them is described. Information provided aims to help decide which is likely to provide the best analgesia depending on the procedure being done. Increase use of these blocks has the potential to improve patient's accessibility to minimally invasive upper abdominal procedures.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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