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Record W3162205838 · doi:10.1002/anr3.12118

Pericapsular nerve group (PENG) block for hip fracture in the emergency department: a case series

2021· article· en· W3162205838 on OpenAlexaff
Andrés Rocha‐Romero, K. Arias‐Mejia, A. Salas‐Ruiz, Philip Peng

Bibliographic record

VenueAnaesthesia Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineHip fractureEmergency departmentMultidisciplinary approachPain managementComplicationMultidisciplinary teamSurgeryAnesthesiaPhysical therapyNursing

Abstract

fetched live from OpenAlex

Guidelines for the management of hip fractures recommend timely identification, analgesia and optimisation, in order to facilitate prompt surgical repair. In achieving these aims, multidisciplinary care is essential. In this case series, we present five patients who received bedside pericapsular nerve group (PENG) blocks by emergency physicians in collaboration with the anaesthesia team for pain management following hip fracture. The PENG block is a novel motor- and opioid-sparing technique, which offers long-lasting analgesia and requires less volume than other blocks. In all of the cases in this series, the blocks were performed successfully in a short period of time, without complication. All patients reported a clinically important reduction in pain scores. Patients with hip fracture are often medically complex, and while early surgery is not always possible, pain management should be addressed from an early point in their hospital admission. Multidisciplinary input into peri-operative pathways can enhance the provision of analgesia in the emergency department, by allowing anaesthetists and emergency physicians to work together for the benefit of these often-frail patients.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.014
GPT teacher head0.270
Teacher spread0.256 · 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 designCase report
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

Citations18
Published2021
Admission routes1
Has abstractyes

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