Use of Median Regression to Predict Hospitalization and Pharmaceutical Costs in a Children’s Hospital
Bibliographic record
Abstract
Aim: The aim of the present study was to explore the impact of the following variables on both hospitalization and pharmaceutical costs in a children’s hospital: patient’s weight, major diagnostic category (MDC), length of stay (LOS), an index reflecting the relative use of healthcare resources during hospitalization and clinical severity. Methods: The CHU Sainte-Justine is a 500-bed mother-child teaching hospital located in Montreal, Qc, Canada. We included in our analysis all inpatient pediatric episodes of care. All data collated describe the episodes of care for the fiscal year 2005-2006 (from April 1, 2005 to March 31, 2006). Two statistical models were developed to explain pharmaceutical cost and total hospitalization cost. The following independent variables were tested in both models: patient’s weight on admission (continuous variable), MDC (discrete variable with a value between 1 and 25, except 12, 13, 14, 15, 20 and 24), LOS, NIRRU and clinical severity (discrete variable with a value of 1 (mild or minor), 2 (moderate), 3 (major or high) or 4 (extreme)) A total of 9 202 episodes met our inclusion criteria. Results: The prediction generic formula for both types of cost is as follows: Cost = Intercept + (b1*weight) + (b2*NIRRU) + (b3*LOS) + (clinical severity x) + (MDC x1) + (MDC x2*LOS) + (LOS*weight) + (MDC x3*weight) + (weight*LOS*MDC x3) where b1, b2, b3 are partial regression coefficients and where x indicates the level of the variable (1, 2, 3 or 4 for clinical severity). Conclusion: This exploratory study shows the feasibility of building a median regression model. In the model proposed, several factors such as diagnosis, disease severity and length of stay were shown to be significant predictors for both hospitalization and pharmaceutical costs. On the other hand, patient’s weight influences only pharmaceutical cost.
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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 | 0.003 | 0.017 |
| 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 | 0.001 |
| Open science | 0.001 | 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".