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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".