Model for the Cost Analysis of Shunted Hydrocephalic Children
Bibliographic record
Abstract
This paper describes a model for forecasting the treatment costs for hydrocephalic patients with ventriculoperitoneal shunts. Modeling with institution-specific or reported failure rates allows the prediction of shunt failure in real and/or theoretical populations. The addition of costing factors (derived from hospitalization, operative and drug costs) to the model allows the derivation of partial or total cost estimates. The effects of varying the failure rate, infection rate, number of new patients, number of lost patients and costing factors can be simulated and measured. Basing this model on data from our institution, decreasing the rate of failure during the first year following shunt insertion or revision has the potential for greater cost savings over time than either decreasing the shunt infection rates or the duration of hospital stay. By combining shunt performance and financial data, an estimate of the cost of the treatment of a population with hydrocephalus, over time, can be derived. These data can be critical for institutional and program budgeting and serve as an estimate of the economic effects of treatment changes proposed in clinical trials.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".