Methods for Patient-Level Costing in the VA System: Are they Applicable to Canada?
Bibliographic record
Abstract
In his article “Determination of VA Health Care Costs,” Barnett (2003 [this issue]) describes various methods available to estimate costs in the U.S. Veterans Affairs (VA) health care system. These methods include direct measurement, pseudo bills combining VA patient-level utilization data and non-VA cost lists, cost functions based on regression analysis using non-VA cost estimates, and average cost databases. The need for these methods arises from the fact that VAhospitals do not prepare patient bills, the primary source of health care costs used in U.S. health economic studies. Barnett (2003) suggests that the principles of cost determination described in his article can be applied to other settings where billing data are not available. This is the case in Canada, where acute-care hospitals are publicly funded through global operating budgets. Because very few hospitals have information systems that produce reliable patient-level costing data, Canadian health economists rely on similar cost-estimation methods to those detailed by Barnett. The parallels between VAhealth care costing methods and those used by Canadian investigators are detailed in the remainder of this commentary.
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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 | 0.027 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.024 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".