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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 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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0100.005
Insufficient payload (model declined to judge)0.0050.002

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 source (direct Gemma or distilled Codex), 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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