Hospital-, Anesthesiologist-, and Patient-level Variation in Primary Anesthesia Type for Hip Fracture Surgery
Bibliographic record
Abstract
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS MANUSCRIPT TELLS US THAT IS NEW: BACKGROUND:: Substantial variation in primary anesthesia type for hip fracture surgery exists. Previous work has demonstrated that patients cared for at hospitals using less than 20 to 25% neuraxial anesthesia have decreased survival. Therefore, the authors aimed to identify sources of variation in anesthesia type, considering patient-, anesthesiologist-, and hospital-level variables. METHODS: Following protocol registration (NCT02787031), the authors conducted a cross-sectional analysis of a population-based cohort using linked administrative data in Ontario, Canada. The authors identified all people greater than 65 yr of age who had emergency hip fracture surgery from April 2002 to March 2014. Generalized linear mixed models were used to account for hierarchal data and measure the adjusted association of hospital-, anesthesiologist-, and patient-level factors with neuraxial anesthesia use. The proportion of variation attributable to each level was estimated using variance partition coefficients and the median odds ratio for receipt of neuraxial anesthesia. RESULTS: Of 107,317 patients, 57,080 (53.2%) had a neuraxial anesthetic. The median odds ratio for receiving neuraxial anesthesia was 2.36 between randomly selected hospitals and 2.36 between randomly selected anesthesiologists. The majority (60.1%) of variation in neuraxial anesthesia use was explained by patient factors; 19.9% was attributable to the anesthesiologist providing care and 20.0% to the hospital where surgery occurred. The strongest patient-level predictors were absence of preoperative anticoagulant or antiplatelet agents, absence of obesity, and presence of pulmonary disease. CONCLUSIONS: While patient factors explain most of the variation in neuraxial anesthesia use for hip fracture surgery, 40% of variation is attributable to anesthesiologist and hospital-level practice. Efforts to change practice patterns will need to consider hospital-level processes and anesthesiologists' intentions and behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".