Effects of injury level and severity on direct costs of care for acute spinal cord injury
Bibliographic record
Abstract
New treatments are being investigated for spinal cord injury (SCI), and any improvement may result in incremental cost savings. The objective of this study was to determine the direct costs of care 2 years after an SCI, stratifying for completeness and level of injury. A retrospective database analysis was carried out using data from the Quebec Trauma Registry, the Quebec Medical Insurance Board, and the Quebec Automobile Insurance Corporation between 1997 and 2007. Excluding individuals sustaining moderate or severe traumatic brain injuries, 481 individuals who sustained an SCI from motor vehicle accidents were identified. Individuals were classified as complete and incomplete in the following categories: C1-C7, C8-T6, T7-L1, L2-S5. Using data from governmental public healthcare organizations makes this study comprehensive. For C1-C7 complete and incomplete spinal cord injuries, the first-year cost was $157 718 and $56 505, respectively (2009 Canadian dollars calculated per patient). Similar differences between complete and incomplete spinal cord injuries were seen for the other groups. Furthermore, for complete injuries, costs were higher for higher levels of injury during both the first and the second year after injury. For incomplete lesions, costs did not differ significantly between groups during the first or the second year. Incomplete spinal cord injuries result in lower healthcare costs compared with complete injuries across all groups during the first 2 years after injury. As less severe levels of injury result in measurably lower costs, the funds spent to reduce the severity or level of SCI could at least partially be recouped through healthcare savings.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".