Statistical analysis on length of stay in hospital
Bibliographic record
Abstract
The rising financial problems of healthcare institutions make studies of resource distribution more and more important and valuable. Among these studies, identification of length of stay of hospital patients (LOS) has attracted many scientists recently since it contributes to better knowledge of hospital costs and helps these institutions control the costs. This paper is devoted to study the length of stay of inpatients in hospital. Although predicting the length of stay is difficul, it is actually useful and benificial if some key factors that have influence on patient length of stay could be determined. This paper will be the basis for a running example that illustrates alternative models of the length of stay of hospital pentients. A total of 1189 episodes, which contains patient records, were analyzed by using some parametric and nonparametric statistical methods. In this study, several factors are first considered and investigated, including date of admission, medical admission unit, dianogsis result, international classification of diseases (icd), age, province, profession, recovery status when discharged, ethnic, and etc. Multiple regression analysis was also carried out for modeling length of stay as a function of several independent variables. Since the number of inpatient hospital stays is concerned, the family of Poisson distributions is used in this study. This approach is also supported by the corresponding histogram. Furthermore, univariate analyses showed that age, province, profession, admission quarter, recovery status when discharged, and diseases significantly influence on LOS. Finally, multivariate analysis of multiple regression model emphasized that type of disease, admission quarter, age group, and profession are the key factors that influence the LOS. These results may have some economic and clinical implications for not only patients but also hospitals.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".