[Characteristics of emergency room admissions of IDF soldiers in northern Israeli hospitals between May 2002 and April 2003].
Bibliographic record
Abstract
BACKGROUND: The hospitalization rate of patients visiting the emergency room depends on various parameters including demography, clinical data and other variables. Long term follow-up of hospitalized IDF soldiers reflected variability in hospital admission rate among hospitals and raises the possibility of vastly inappropriate hospitalizations. AIM: The aim of this study was to characterize the admission policy for IDF soldiers in various hospitals, to assess the rate of inappropriate hospitalizations, and to evaluate these costs to the Medical Corps. METHODS: Three hospitals in northern Israel were screened between May 2002 and April 2003, one urban tertiary care hospital (A) and two rural hospitals of different sizes (B and C). One fifth of the discharge forms were randomly screened. Every admission was given a risk evaluation based only on clinical data found upon presentation in the emergency room. One abnormal finding rendered the admission to be considered as appropriate. The rate of admission, lengths of stay, as well as clinical data and utilization of diagnostic tests were recorded and compared. RESULTS: During the study period 65 +/- 1105, 39 +/- 536, and 54 +/- 485 Emergency Room visits were made monthly in hospitals A, B and C, respectively. A total of 1.0% +/- 6.5. 1.4% +/- 12.6 and 2.7% +/- 11.8 of these visits resulted in hospitalization in hospitals A, B and C, respectively. Approximately one quarter of the patients admitted to hospital A were low risk, compared to more than half in the rural hospitals. Low risk admissions were one day shorter in the urban hospital, whereas high risk admissions had the same length of stay. The cost of low risk admissions in the urban hospital was about half of its cost in any of the rural hospitals. The estimated annual cost of low risk admission in all three hospitals screened was about $US 1 million. CONCLUSIONS: The admission rate in the urban hospital was half of its rate in the rural hospitals. Overall, 27% less low risk admissions were made in the urban hospital. The rural hospitals tendency to admit more low risk patients was compounded with their tendency to lengthen these admissions. The reason for this behaviour is not clear, although it may be related to differences in experience and clinical knowledge, consideration of the distance between the hospital and the soldier's home or unit, and financial admission policies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".