Using Risk Scores to Estimate Lower Extremity Fragility Fracture Risk among Individuals with Chronic Spinal Cord Injury: A Preliminary Model
Bibliographic record
Abstract
Objectives To develop SCI-FX, a risk score to estimate 5-year lower extremity fragility fracture risk among patients living with chronic spinal cord injury (cSCI). Methods Adults with traumatic cSCI ( n = 90) participated in a 2-year prospective longitudinal cohort study describing bone mineral density (BMD) change and fracture incidence conducted at the Lyndhurst Centre (University Health Network), University of Waterloo, and Physical Disability Rehabilitation Institute of Québec City. Prior publication and clinical intuition were used to identify fragility fracture risk factors including prior fragility fracture, years post-injury, motor complete injury (AIS A/B), benzodiazepine use, opioid use, and parental osteoporosis. We conducted bivariate analyses to identify variables associated with fracture. Multiple logistic regressions were performed using fragility fracture incidence as the dependent variable and all variables from the univariate analyses with a highly liberal p value at 0.2. Using the odds ratios (ORs) from the multiple logistic regression model, a point system for fragility fracture risk score was developed, and the odds of fracture for each point was estimated. Results All initial variables, with the exception of benzodiazepine exposure, were included in the final model. Conclusion We identified a simple preliminary model for clinicians to estimate 5-year fracture risk among patients with cSCI based on their total score.
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 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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".