Secondary Complications in SCI Across the Continuum: Using Operations Research to Predict the Impact and Optimize Management Strategies
Bibliographic record
Abstract
Secondary complications following traumatic spinal cord injury (tSCI) have a tremendous impact on quality of life and health care costs. Although some complications result from the injury itself, many originate from the care provided; complications arising early in the tSCI journey can predispose an individual to recurrence later. To measure the total impact of secondary complications on patient outcomes and health care costs, all the stages of care, from first response to life in the community, must be spanned. Interventions to ameliorate secondary complications need to consider the effects on the whole system and not just individual phases of care; however, such an approach is not common in the literature. To measure the impact of complications as well as the effect of proposed interventions, a partnership between clinical researchers and operations research professionals was formed to develop a discrete-event simulation model of the entire continuum of tSCI care. In this article, we focus on the part of the model concerning common secondary complications (eg, pressure ulcers, pneumonia). We first describe early results from the model, discuss how the effects from the complications impact care throughout the tSCI continuum, and review assumptions of the model. The article concludes with a discussion as to the possible uses of the model, their strengths/limitations, and future directions.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".