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Record W4323036005 · doi:10.1002/pmrj.12966

Ultrasound‐guided injection of the elbow: Cadaveric description for the proximal to distal approach

2023· article· en· W4323036005 on OpenAlexaff
Vincenzo Ricci, Kamál Mezian, Ke‐Vin Chang, Nimish Mittal, Murat Kara, Ondřej Naňka, Levent Özçakar

Bibliographic record

VenuePM&R · 2023
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCadaveric spasmElbowCadaverDissection (medical)AnatomyUltrasoundSurgeryRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Ultrasound (US) guided intra-articular elbow injections are commonly performed in clinical practice. OBJECTIVE: To describe a proximal to distal approach for US-guided intra-articular elbow injection. DESIGN: Cadaveric study. SETTINGS: Academic institution. METHODS: Both elbows of a single cadaver were injected with green-colored water-diluted latex dye using the US-guided proximal to distal approach. In the left elbow, the needle was kept in situ; in the right elbow, the needle was removed. Subsequently, a layer-by-layer anatomical dissection was performed in both elbows. MAIN OUTCOME MEASURES: Presence and distribution of the latex dye and location of the needle tip within the elbow joint capsule. RESULTS: Anatomical dissection of both elbows confirmed the correct intra-articular position of the needle tip in the left elbow as well as correct placement of the latex dye bilaterally. During layer-by-layer dissection of the left elbow, the position of the radial nerve was observed anterior to the needle. CONCLUSIONS: This cadaveric observation demonstrated that the US-guided proximal to distal approach is a convenient technique to access the elbow joint. Compared to the previously described techniques, the in-plane, proximal to distal approach may provide excellent needle visibility during the entire procedure, precisely targeting the articular space. The preliminary data need to be validated in additional clinical 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

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 score0.560
Threshold uncertainty score0.172

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 head0.055
GPT teacher head0.286
Teacher spread0.232 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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