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Record W3176926027 · doi:10.1136/rapm-2021-102735

Regional anesthesia and acute compartment syndrome: principles for practice

2021· article· en· W3176926027 on OpenAlexafffund
Tim Dwyer, David Burns, Aaron Nauth, Kaitlin Kawam, Richard Brull

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicMuscle and Compartmental Disorders
Canadian institutionsToronto Western HospitalSt. Michael's HospitalWomen's College HospitalUniversity of TorontoMount Sinai Hospital
FundersWomen's College Hospital
KeywordsMedicineOrthopedic surgeryAcute painAnesthesiaCompartment (ship)Intensive care medicineIschemiaClinical PracticeSurgeryPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Acute compartment syndrome (ACS) is a potentially reversible orthopedic surgical emergency leading to tissue ischemia and ultimately cell death. Diagnosis of ACS can be challenging, as neither clinical symptoms nor signs are sufficiently sensitive. The cardinal symptom associated with ACS is pain reported in excess of what would otherwise be expected for the underlying injury, and not reasonably managed by opioid-based analgesia. Regional anesthesia (RA) techniques are traditionally discouraged in clinical settings where the development of ACS is a concern as sensory and motor nerve blockade may mask symptoms and signs of ACS. This Education article addresses the most common trauma and elective orthopedic surgical procedures in adults with a view towards assessing their respective risk of ACS and offering suggestions regarding the suitability of RA for each type of surgery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0020.008
Scholarly communication0.0050.007
Open science0.0030.005
Research integrity0.0080.018
Insufficient payload (model declined to judge)0.0070.008

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 head0.049
GPT teacher head0.310
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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 routes2
Has abstractyes

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