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Record W2048856357 · doi:10.1213/ane.0b013e3181a2a252

Does Ultrasound Guidance Improve the Success Rate of Infraclavicular Brachial Plexus Block When Compared with Nerve Stimulation in Children with Radial Club Hands?

2009· article· en· W2048856357 on OpenAlexaboutno aff
Vrushali Ponde, Sandeep Diwan

Bibliographic record

VenueAnesthesia & Analgesia · 2009
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBrachial plexusAnesthesiaRadial nerveUltrasoundBupivacaineSurgeryStimulationBrachial plexus blockAnalgesicNerve blockRadiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The classical response to nerve stimulation may be altered in cases of radial club hand. Ultrasound guidance may prove to be a useful tool in such situations. In this study, we compared the success rate of ultrasound-guided infraclavicular brachial plexus block with nerve stimulation for children undergoing radial club hand repair. METHODS: Fifty children, aged 1-2 yr, undergoing radial club hand repair were randomly assigned to receive infraclavicular brachial plexus block guided by nerve stimulator (Group NS) or ultrasound (Group U) in combination with light general anesthetic. Bupivacaine 0.5 mL/kg of 0.5% was injected in both groups. Pain response to surgical stimulus was considered as block failure. The Children's Hospital Eastern Ontario Pain Scale pain score was recorded at 1, 4, 6, 8, and 10 postoperative hours. RESULTS: In Group NS, the blocks were successful in 16 of 25 patients (64%), whereas in Group U, 24 of 25 patients had successful blocks (P = 0.0053). There was no difference in the time to first analgesia or analgesic consumption in the 10-h study period. CONCLUSION: Ultrasound-guided infraclavicular brachial plexus block improves the success rate in patients with radial club hands when compared with nerve stimulation in patients undergoing radial club hand correction.

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)0.001
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 score0.002
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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

Citations46
Published2009
Admission routes1
Has abstractyes

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