Forecasting Financial Resources for Future Traumatic Spinal Cord Injury Care Using Simulation Modeling
Bibliographic record
Abstract
Survivors of traumatic spinal cord injury (tSCI) have intense healthcare needs during acute and rehabilitation care and often through the rest of life. To prepare for a growing and aging population, simulation modeling was used to forecast the change in healthcare financial resources and long-term patient outcomes between 2012 and 2032. The model was developed with data from acute and rehabilitation care facilities across Canada participating in the Access to Care and Timing project. Future population and tSCI incidence for 2012 and 2032 were predicted with data from Statistics Canada and the Canadian Institute for Health Information. The projected tSCI incidence for 2012 was validated with actual data from the Rick Hansen SCI Registry of the participating facilities. Using a medium growth scenario, in 2032, the projected median age of persons with tSCI is 57 and persons 61 and older will account for 46% of injuries. Admissions to acute and rehabilitation facilities in 2032 were projected to increase by 31% and 25%, respectively. Because of the demographic shift to an older population, an increase in total population life expectancy with tSCI of 13% was observed despite a 22% increase in total life years lost to tSCI between 2012 and 2032. Care cost increased 54%, and rest of life cost increased 37% in 2032, translating to an additional CAD $16.4 million. With the demographics and management of tSCI changing with an aging population, accurate projections for the increased demand on resources will be critical for decision makers when planning the delivery of healthcare after tSCI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".