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Record W4361217147 · doi:10.1136/rapm-2023-104454

General anesthesia is an acceptable choice for hip fracture surgery

2023· article· en· W4361217147 on OpenAlexaff
Eric S. Schwenk, Colin J. L. McCartney

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAnesthesiaHip fractureInternal medicineOsteoporosis

Abstract

fetched live from OpenAlex

The debate over the optimal type of anesthesia for hip fracture surgery continues to rage. While retrospective evidence in elective total joint arthroplasty has suggested a reduction in complications with neuraxial anesthesia, previous retrospective studies in the hip fracture population have been mixed. Recently, two multicenter randomized, controlled trials (REGAIN and RAGA) have been published that examined delirium, ambulation at 60 days, and mortality in patients with hip fractures who were randomized to spinal or general anesthesia. These trials enrolled a combined 2,550 patients and found that spinal anesthesia did not confer a mortality benefit nor a reduction in delirium or greater proportion who could ambulate at 60 days. While these trials were not perfect, they call into question the practice of telling patients that spinal anesthesia is a "safer" choice for their hip fracture surgery. We believe a risk/benefit discussion should take place with each patient and that ultimately the patient should choose his or her anesthesia type after being informed of the state of the evidence. General anesthesia is an acceptable choice for hip fracture 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)0.004
metaresearch head score (Gemma)0.021
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: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.060
GPT teacher head0.318
Teacher spread0.258 · 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
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

Citations6
Published2023
Admission routes1
Has abstractyes

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