Rehabilitation after lower limb injury: development of a predictive score (RALLI score)
Bibliographic record
Abstract
BACKGROUND: The purpose of our study was to identify the risk factors associated with the need for inpatient rehabilitation after lower limb injury to develop a predictive scoring tool for early identification of such patients. METHODS: We followed a prospective cohort of patients admitted to a level 1 trauma centre. Data were collected through chart review and a self-administered questionnaire on sociodemographics, patient living environment, pretrauma status, injury and treatment received. We compared patients who were discharged home with those going to rehabilitation after acute care. Analysis consisted of bivariate comparisons and logistic regression. RESULTS: Our study included 160 patients with a mean age of 56 years. A total of 40% were discharged to an inpatient rehabilitation centre. Factors associated with inpatient rehabilitation were low preinjury physical health status, concomitant injury of the upper limbs, bilateral lower limb injury, the use of a walking aid before injury, head injury and femur or pelvic fractures. We created a predictive score using the top 3 risk factors: upper limb injury, bilateral lower limb injury and presence of femoral or pelvic fractures. The chance of needing inpatient rehabilitation rose from 14% with 0 factors to 47% with 1 factor and 96% with 2 factors. CONCLUSION: Rehabilitation planning should begin for patients exhibiting at least of 3 risk factors at the time of admission to acute care. Prospective validation of the tool is needed, but it has the potential to orient the multidisciplinary team's decision on rehabilitation needs postdischarge.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".