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Record W3043345681 · doi:10.24908/pocus.v5i1.14223

Interscalene block in an anesthetized adult with hypertrophic obstructive cardiomyopathy undergoing clavicle fracture reduction

2020· article· en· W3043345681 on OpenAlexaffvenue
Anthony M.‐H. Ho, Joel L. Parlow, René Allard, Michael McMullen, Glenio B. Mizubuti

Bibliographic record

VenuePOCUS Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineObstructive cardiomyopathyAnesthesiaReduction (mathematics)Nerve blockSurgeryCardiologyHypertrophic cardiomyopathy

Abstract

fetched live from OpenAlex

Whether regional anesthesia procedures should be performed in heavily sedated/anesthetized adults remains controversial. One of the purported advantages of performing regional nerve blocks in conversant patients is early warning against major nerve injury and, arguably, early detection of local anesthetic systemic toxicity. A 60-year-old man with hypertrophic obstructive cardiomyopathy (HOCM) underwent a clavicle fracture repair under general anesthesia. Intraoperative transesophageal echocardiography revealed dynamic left ventricular outflow track obstruction and systolic anterior motion of the posterior mitral valve leaflet. In part based on such echo findings, he received an ultrasound-guided interscalene plus a superficial cervical plexus block for postoperative analgesia prior to emergence from general anesthesia. Given the lack of robust data on the safety of ultrasound-guided regional techniques in heavily sedated/anesthetized adults, we use the example of echographic evidence of significant HOCM to argue for a pragmatic and individualized approach when faced with unusual situations in which the pros of such an approach may outweigh the cons - in this case for performing an interscalene block on an anesthetized adult.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.498

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 integrity0.0000.001
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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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.

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

Citations2
Published2020
Admission routes2
Has abstractyes

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