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Record W4400646965 · doi:10.1213/xaa.0000000000001816

Phrenic Nerve Block for Diaphragmatic Pain: Case Report

2024· article· en· W4400646965 on OpenAlexaff
Chanon Thanaboriboon, Marta A. Vargas, Konstantinos Alexopoulos, Jordi Pérez

Bibliographic record

VenueA&A Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineDiaphragmatic breathingPhrenic nerveIrritationAnesthesiaNerve blockChronic painBlockadeSurgeryRespiratory systemPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Referred chronic shoulder pain may arise from diaphragmatic irritation. It can potentially be alleviated by blockade of the phrenic nerve. There is literature describing its use in acute pain conditions; yet for chronic pain, there are no reports. We present 2 cases of chronic diaphragmatic irritation causing ipsilateral referred shoulder pain. Patients experienced significant pain relief and a reduction in opioid consumption after receiving an ultrasound-guided phrenic nerve block. While the phrenic nerve block shows promise for pain relief, carefully evaluating its benefits and risks is recommended before considering its application in selected cases.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.027
GPT teacher head0.333
Teacher spread0.306 · 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 designCase report
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

Citations3
Published2024
Admission routes1
Has abstractyes

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