MétaCan
Menu
Back to cohort
Record W3155680945 · doi:10.1002/anr3.12112

Pain management using a novel hybrid technique of perineural stimulation combined with regional anaesthesia through a stimulating perineural catheter for below knee amputation

2021· article· en· W3155680945 on OpenAlexaff
Vivian Ip, Rakesh V. Sondekoppam, Ban C. H. Tsui

Bibliographic record

VenueAnaesthesia Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineAnesthesiaCatheterNeuromodulationAdductor canalAmputationPercutaneousSciatic nerveOpioidSurgeryStimulationTotal knee arthroplastyInternal medicine

Abstract

fetched live from OpenAlex

Pain after amputation can be difficult to manage due to its complex aetiology. A multimodal approach to analgesia, including regional anaesthetic techniques, is advised. However, optimal pain management cannot always be achieved, and high doses of opioid analgesics may contribute to adverse effects. We describe the management of an elderly patient with significant co-morbidities undergoing below knee amputation. Pre-operatively, a popliteal sciatic stimulating perineural catheter and a femoral non-stimulating perineural catheter were placed. When pain control was suboptimal on the first postoperative day, a combination of local anaesthetic and a brief period of peripheral nerve stimulation through the sciatic stimulating perineural catheter was used to augment pain control, thereby avoiding additional opioid use. Although nerve stimulation utilising specialised equipment, such as percutaneous stimulator electrodes, has been previously described in acute pain medicine, we demonstrate the use of a novel hybrid technique which combines nerve stimulation through a perineural catheter and local anaesthetic. Further research is warranted to explore the utility of this neuromodulation technique in clinical practice.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
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 head0.032
GPT teacher head0.283
Teacher spread0.252 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAnaesthesia ReportsSame topicAnesthesia and Pain ManagementFrench-language works237,207