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Record W4417321354 · doi:10.1136/rapm-2025-107388

Ultrasound-guided motor-sparing forearm blocks for hand surgery: surgical and anesthetic perspectives

2025· article· en· W4417321354 on OpenAlexaff
Chao-Ying Kowa, Behdad Ravarian, Heather L. Baltzer, Ki Jinn Chin

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsUniversity of TorontoToronto Western Hospital
Fundersnot available
KeywordsForearmAnestheticSurgical proceduresAnesthetic AgentSurgical incision

Abstract

fetched live from OpenAlex

INTRODUCTION: Certain specialized hand surgery procedures benefit from intraoperative motor testing and patient-demonstrated active range of motion. This requires motor-sparing regional anesthesia of the hand using targeted ultrasound-guided nerve blocks in the forearm. OBJECTIVE: We present a joint surgical and anesthetic perspective covering the utility and indications for motor-sparing forearm blocks. We describe the anatomical principles and technical details of their performance and discuss other considerations for surgical and anesthetic success. FINDINGS: Patient selection and expectation setting are critical for success. Four terminal nerves must be blocked: median nerve, ulnar nerve, superficial radial nerve, and lateral antebrachial cutaneous nerve. These must be targeted distal to origin of motor branches to extrinsic muscles of the hand, with precise deposition of limited volumes of local anesthetic. The nerves can be readily identified with ultrasound imaging by their predictable anatomical relationship to specific muscles and blood vessels. CONCLUSION: Intraoperative motor testing in complex surgical repair of the hand is associated with improved surgical outcomes, as well as greater patient satisfaction. Ultrasound-guided motor-sparing forearm blocks are a safe and effective method for achieving surgical anesthesia and optimal operating conditions in this context.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.025
GPT teacher head0.299
Teacher spread0.273 · 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.

Study designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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