The Impact of Neuroanesthesia Fellowship Training and Anesthesiologist-Surgeon Dyad Volume on Patient Outcomes in Adult Spine Surgery: A Population-Based Study
Bibliographic record
Abstract
INTRODUCTION: Extensive research has explored the impact of surgeons' characteristics on patient outcomes; however, the influence of anesthesiologists remains understudied. We performed a population-based retrospective cohort study to investigate the impact of anesthesiologists' characteristics on in-hospital morbidity after spine surgery. METHODS: Adult patients who underwent spine surgery at the London Health Science Centre, Ontario, Canada between January 1, 2010 and June 30, 2023 were included in this study. Data was extracted from the local administrative database. Five anesthesiologists' characteristics (neuroanesthesia fellowship and residency training backgrounds, surgeon familiarity, annual case volume, and sex) were examined as primary exposures. The primary outcome was composite in-hospital morbidity, encompassing 141 complications. Multivariable logistic regression was performed to assess the association between anesthesiologists' characteristics and postoperative morbidity with adjustment of patients' sex, Charlson Comorbidities Index, surgical complexity, and surgeon characteristics. RESULTS: A total of 7692 spine surgeries were included in the analysis. Being a neuroanesthesia fellowship-trained anesthesiologist and high anesthesiologist-surgeon annual dyad volume were associated with reduction in in-hospital comorbidity; adjusted odds ratio (95% CI) of 0.58 (0.49-0.69; P <0.001) and 0.93 (0.91-0.95; P <0.001), respectively. Conversely, anesthesiologist annual case volume, characteristics of residency training and anesthesiologist sex showed only nuanced associations with outcomes. CONCLUSIONS: Neuroanesthesia fellowship training and high surgeon-anesthesiologist dyad familiarity was associated with a reduction in in-hospital morbidity following spine surgery. These findings underscore the superiority of structured fellowship education over case exposure experience alone, advocate for dedicated neuroanesthesia teams with high surgeon-anesthesiologist dyad volume and recognize neuroanesthesia as a crucial subspecialty in spine surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".