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Record W2910549845 · doi:10.1097/aap.0000000000000178

Neurological Complications Related to Elective Orthopedic Surgery

2015· review· en· W2910549845 on OpenAlexaff
Tim Dwyer, Patrick Henry, Phantila Cholvisudhi, Vincent Chan, John Theodoropoulos, Richard Brull

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2015
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreUniversity Health NetworkUniversity of TorontoWomen's College HospitalToronto Western Hospital
Fundersnot available
KeywordsMedicineOrthopedic surgeryElbowShoulder surgerySurgeryArthroscopyAnesthesia

Abstract

fetched live from OpenAlex

Many anesthesiologists are unfamiliar with the rate of surgical neurological complications of the shoulder and elbow procedures for which they provide local anesthetic-based anesthesia and/or analgesia. Part 1 of this narrative review series on neurological complications of elective orthopedic surgery describes the mechanisms and likelihood of peripheral nerve injury associated with some of the most common shoulder and elbow procedures, including open and arthroscopic shoulder procedures, elbow arthroscopy, and total shoulder and elbow replacement. Despite the many articles available, the overall number of studied patients is relatively low. Large prospective trials are required to establish the true incidence of neurological complications following elective shoulder and elbow surgery. WHAT'S NEW: As the popularity of regional anesthesia increases with the development of ultrasound guidance, anesthesiologists should have a thoughtful understanding of the nerves at risk of surgical injury during elective shoulder and elbow procedures.

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)0.000
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.112
GPT teacher head0.356
Teacher spread0.244 · 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
GenreReview

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

Citations47
Published2015
Admission routes1
Has abstractyes

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