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Record W4379508327 · doi:10.1097/aln.0000000000004604

Comparative Effectiveness Research on Spinal versus General Anesthesia for Surgery in Older Adults

2023· review· en· W4379508327 on OpenAlexaff
Mark D. Neuman, Frederick E. Sieber, Derek Dillane

Bibliographic record

VenueAnesthesiology · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of Alberta Hospital
Fundersnot available
KeywordsMedicineSpinal anesthesiaRandomized controlled trialSpinal surgeryArthroplastyMEDLINEComparative effectiveness researchEvidence-based medicineKnee surgeryPhysical therapyClinical trialAnesthesiaSurgeryAlternative medicineOsteoarthritis

Abstract

fetched live from OpenAlex

Comparative effectiveness research aims to understand the benefits and harms of different treatments to assist patients and clinicians in making better decisions. Within anesthesia practice, comparing outcomes of spinal versus general anesthesia in older adults represents an important focus of comparative effectiveness research. The authors review methodologic issues involved in studying this topic and summarize available evidence from randomized studies in patients undergoing hip fracture surgery, elective knee and hip arthroplasty, and vascular surgery. Across contexts, randomized trials show that spinal and general anesthesia are likely to be equivalent in terms of safety and acceptability for most patients without contraindications. Choices between spinal and general anesthesia represent "preference-sensitive" care in which decisions should be guided by patients' preferences and values, informed by best available evidence.

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)0.013
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0070.007
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.329
GPT teacher head0.505
Teacher spread0.177 · 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 designSystematic review
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

Citations12
Published2023
Admission routes1
Has abstractyes

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