Understanding Length of Stay after Spinal Cord Injury: Insights and Limitations from the Access to Care and Timing Project
Bibliographic record
Abstract
Costs associated with initial hospitalization following spinal cord injury (SCI) are substantial, and a major driver of costs is the length of stay (LOS); that is, the time that the injured individual remains hospitalized prior to community reintegration. Our aim was to study the factors and variables that contribute to LOS following traumatic SCI. Modeling (process mapping of the SCI healthcare delivery system in Canada and discrete event simulation) and regression analysis using a national registry of individuals with acute traumatic SCI in Canada, existing databases, and peer-reviewed literature were used to examine the driver of LOS following traumatic SCI. In different jurisdictions, there is considerable variation in the definitions and methods used to determine LOS following SCI. System LOS can be subdivided into subcomponents, and progression through these is not unidirectional. Modeling reveals that healthcare organization and processes are important contributors to differences in LOS independent of patient demographics and injury characteristics. Future research is required to identify and improve understanding of contributors to LOS following traumatic SCI. This will help enhance system performance. Work in this area will be facilitated by the adoption of common terminology and definitions, as well as by the use of simulations and modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".