Observations on Surgical Demand Time Series
Bibliographic record
Abstract
BACKGROUND: Surgical scheduling is complicated by both naturally occurring and human-induced variability in the demand for surgical services. Surgical demand time series are decomposed into periodic, lagged, and linear trends with frequent occurrences of nonconstant variations in mean and variance. The authors used time series methods to model surgical demand time series in order to improve the scheduling of scarce surgical resources. METHODS: With institutional approval, the authors studied 47,752 surgeries undertaken at a large academic medical center. They initially extracted periodic information from the time series using two frequency domain techniques: the harmonic F test and the multitaper test. They subsequently extracted lagged (correlated) behavior using a seasonal autoregressive integrated moving average model. Finally, they used moving variance filters on the residuals to identify variance in the time series that coincided with major US holidays. RESULTS: Linear terms such as periodic cycles, trends, and daily and weekly lags explained 80% of the variance in the raw time series. In the residuals, the authors used moving variance filters to detect nonlinear variance artifacts that correlated with surgical activities on specific US holidays. CONCLUSIONS: After extracting linear terms, the remaining variance was attributable to a combination of nonlinear and unexplained random events. The authors used the term holiday variance to describe a specific nonlinear disturbance in surgical demand attributable to statutory US holidays. Resolving these holiday variances may assist in management and scheduling of scarce surgical personnel and resources.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".