Modeling daily veterinary anesthetist patient care hours and probabilities of exceeding critical thresholds
Bibliographic record
Abstract
OBJECTIVE: Use a referral dental clinic model to study how to calculate accurate 95% upper confidence limits for probabilities of workloads (total case duration, including turnover time) exceeding allocated times. ANIMALS: Dogs and cats undergoing dental treatments. METHODS: Managerial data (procedure date and duration) collected over 44 consecutive operative workdays were used to calculate the daily anesthetist workload. Workloads were compared with a normal distribution using the Shapiro-Wilk test, serial correlation was examined by runs test, and comparisons among weekdays were made using the Kruskal-Wallis test. The 95% confidence limits for normally distributed workloads exceeding allocated times were estimated with a generalized pivotal quantity. The impact of a number of procedures was assessed with scatterplots, Pearson linear correlation coefficients, and multivariable linear regression. RESULTS: Mean anesthetist's workload was normally distributed (Shapiro-Wilk P = .25), without serial correlation (P = .45), and without significant differences among weekdays (P = .52). Daily workload, mean 9.39 hours and SD 3.06 hours, had 95% upper confidence limit of 4.47% for the probability that exceeding 16 hours (ie, 8 hours per each of 2 tables). There was a strong positive correlation between daily workload and the end of the workday (r = .85), significantly larger than the correlation between the end of the workday and the number of procedures (r = .64, P < .0001). CLINICAL RELEVANCE: There are multiple managerial applications in veterinary anesthesia wherein the problem is to estimate risks of exceeding thresholds of workload, including the costs of hiring a locum, scheduling unplanned add-on cases, planning for late discharge of surgical patients to owners, and coordinating anesthetist breaks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".