Developing a Lifetime Cost Calculator for Spinal Cord Injury: The SCI Cost Calculator
Bibliographic record
Abstract
Study DesignHealth economic model.ObjectivesSignificant work has been done to estimate the cost of spinal cord injury (SCI) from a health system perspective. Most of this work, however, does not establish the true cost to the individual living with SCI. This study aimed to develop a cost calculator reflecting both initial health care costs as well as lifetime costs with direct input from people living with SCI.MethodsCosts were seperated into initial costs and lifetime costs. For initial costs, estimates were obtained from Canadian SCI health care data and the literature, and were then adjusted for inflation to 2024. For lifetime costs, input from individuals living with SCI and clinical input were collected. Initial and lifetime costs were integrated into a novel, user-friendly cost calculator.ResultsThe SCI Cost Calculator provides an estimated lifetime SCI cost ranging from $1.9 M (older person living with incomplete SCI) to over $10 M 2024 Canadian dollars (younger person living with complete SCI) which is substantially higher than existing estimates. Initial costs can be as little as 3% of the lifetime cost for younger people or as high as 24% of the lifetime cost for older individuals.ConclusionCurrent cost estimates greatly understate the cost of living with SCI. To understand the support needed for persons living with SCI, an economic model that accurately reflects the costs of living with SCI is critical. Future work includes engaging the wider SCI community to enhance accuracy and validity of the cost domains identified.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".