VARIABLES INFLUENCING MEDICAL DECISIONS IN A CLINICAL REHABILITATION HOSPITAL
Bibliographic record
Abstract
The discharge from the hospital represents a complex process that should assess the patient’s ability to adapt outside of the medical setting. Material and methods: This paper analyses the medical status of the patients admitted to a rehabilitation clinic, at the moment of discharge, considering a number of influencing factors, such as age, sex, place of origin, length of stay (LOS) in the hospital. In order to achieve this purpose, we conducted a study on 136 patients discharged from the Clinical Rehabilitation Hospital Iasi in 2016 using an econometric modelling. Results: Our findings reveal that the medical status of the patients at the time of discharge was not influenced by the sex or place of origin. Also, with an increase in patient’s age by one year, the estimated chances of a patient not to be cured at discharge decreases by 3.5%. In addition, with an increase in the patient’s length of stay in the hospital by one day, the estimated chances of a patient not being cured at discharge time increases by 8.1%. Conclusions: It was found that the age and the length of admission in the hospital has a significant influence on the disease stabilization, even if the patients are not fully cured at discharge time. Because the research only considered data from a single regional hospital, it limits the conclusions that can be drawn from the sample.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".