Estimation of unpredictable hospital demand variations in two Piraeus public hospitals, Greece
Bibliographic record
Abstract
The scope of this paper is to estimate the unpredictable hospital demand variations in two public general hospitals in Piraeus, Greece. We used daily emergency admissions data between 1/1/2001 to 31/12/2005. To measure unpredictable hospital demand we used both a univariate Autoregressive Moving Average model and a multivariate time series model. In the latter one, four explanatory variables are tested: the weekend effect, the duty effect, the summer holiday effect, and the official holiday effect. The variance of forecasted residuals of each hospital regression for every day provides the estimated unpredictable demand. The study verifies that daily emergency admissions are characterized by seasonal and weekly variations. In Tzaneio hospital, the unexpected part of emergency admissions increases over the five-year period, while in Nikaias hospital, it reduces. From the univariate analysis, it was found that the unpredictable part of admissions is not the same for the two hospitals and it also varies over the years. The variations of unpredictable hospital demand have increased by about 45% in Tzaneio hospital and have decreased by about 10% in Nikaias hospital. The results from the multivariate analysis show that the variations in unpredictable daily demand in Tzaneio hospital have less than doubled. On the contrary, the variations in unpredictable daily demand in Nikaias hospital have decreased by 20%. Due to these trends, a general conclusion is that at the beginning of the time period under investigation there are large variations in unpredictable demand between the two hospitals, which become smaller as we move on to 2005.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".