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Record W2346421932 · doi:10.1111/anae.13486

Comparison between ultrasound and nerve stimulation for infraclavicular catheter placement – a reply

2016· letter· en· W2346421932 on OpenAlexaffabout
Shalini Dhir

Bibliographic record

VenueAnaesthesia · 2016
Typeletter
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsSt. Joseph’s Healthcare HamiltonSt Joseph's Health Care
Fundersnot available
KeywordsMedicineOverweightCatheterBlockadeConfoundingPopulationRegional anesthesiaAnesthesiaStimulationNerve stimulatorBody mass indexNerve stimulationObesityNerve blockPeripheral nerveSurgeryInternal medicineAnatomy

Abstract

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I thank Drs. Dalay and Jagannathan for their interest in our study, which compared nerve stimulation with ultrasound guidance for infraclavicular catheter placement 1. We agree that patients with higher BMI may benefit from regional anesthesia, even though the risk of block failure is higher 2, 3. We excluded very obese patients (BMI > 35 kg.m−2) in order to reduce the contribution of obesity as a confounding factor in our comparison. However, we did include overweight (BMI 25.0–29.9 kg.m−2) and obese (BMI 30.0–34.9 kg.m−2) participants, as it would have been unreasonable to exclude them, given rates of obesity within the general population. Effectively, therefore, we excluded approximately 8–11% of potential participants, based on Canadian population statistics for obesity 4. With regard to using ultrasound and nerve stimulation together, the present study originated from a previous, small study in which we compared dual guidance with nerve stimulation 5. As the usefulness of dual guidance is debatable 6, 7, we designed the present study to compare only one method of guidance with the other. We retained equipoise when comparing complications between the two techniques, as there appears to be no difference in neurological injury between ultrasound and nerve stimulator-guided peripheral nerve blockade 8.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.181
Threshold uncertainty score1.000

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 head0.031
GPT teacher head0.308
Teacher spread0.277 · 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
GenreCommentary

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

Citations0
Published2016
Admission routes2
Has abstractyes

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