Perioperative Mortality, 2010 to 2014
Bibliographic record
Abstract
BACKGROUND: The National Anesthesia Clinical Outcomes Registry collects demographic and outcome data from anesthesia cases, with the goal of improving safety and quality across the specialty. The authors present a preliminary analysis of the National Anesthesia Clinical Outcomes Registry database focusing on the rates of and associations with perioperative mortality (within 48 h of anesthesia induction). METHODS: The authors retrospectively analyzed 2,948,842 cases performed between January 1, 2010, and May 31, 2014. Cases without procedure information and vaginal deliveries were excluded. Mortality and other outcomes were reported by the anesthesia provider. Hierarchical logistic regression was performed on cases with complete information for patient age group, sex, American Society of Anesthesiologists physical status, emergency case status, time of day, and surgery type, controlling for random effects within anesthesia practices. RESULTS: The final analysis included 2,866,141 cases and 944 deaths (crude mortality rate, 33 per 100,000). Increasing American Society of Anesthesiologists physical status, emergency case status, cases beginning between 4:00 PM and 6:59 AM, and patient age less than 1 yr or greater than or equal to 65 yr were independently associated with higher perioperative mortality. A post hoc subgroup analysis of 279,154 patients limited to 22 elective case types, post hoc models incorporating either more granular estimate of surgical risk or work relative value units, and a post hoc propensity score-matched cohort confirmed the association with time of day. CONCLUSIONS: Several factors were associated with increased perioperative mortality. A case start time after 4:00 PM was associated with an adjusted odds ratio of 1.64 (95% CI, 1.22 to 2.21) for perioperative death, which suggests a potentially modifiable target for perioperative risk reduction. Limitations of this study include nonstandardized mortality reporting and limited ability to adjust for missing data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".