Predicting Discharge of Trauma Survivors to Rehabilitation
Bibliographic record
Abstract
OBJECTIVES: To conduct a population-based survey among trauma survivors on accessibility to rehabilitation services in metropolitan, urban, and rural areas in Quebec (Canada), we attempted to use trauma registries as a sampling frame of subjects discharged to rehabilitation. Discharge destinations were inaccurate in many registries, preventing straightforward identification of the survey subjects. Using the best registry data, we aimed to identify predictors of rehabilitation discharge and to use them to specify a reliable sampling frame for the survey. DESIGN: A logistic predictive model of rehabilitation discharge was developed. This model was applied to data from metropolitan, urban, and rural trauma centers to identify all subjects predicted to be discharged to a rehabilitation facility. RESULTS: Age, acute-care length of stay, injury-severity score, lower-limb injuries, and seven other predictors were included in the model that generated an area under the ROC curve (AUC) of 0.83 and a classification accuracy of 76.6%. The metropolitan, urban, and rural frames were slightly different. They included, respectively, 808, 798, and 929 subjects. CONCLUSIONS: The procedure helped us bypass largely inaccurate data from trauma registries. The sampling frames reflected severely injured trauma survivors who were likely to have been referred to postacute rehabilitation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".