Prevalence and Impact of Fractures in Persons with Spinal Cord Injuries: A Population-Based Study Comparing Fracture Rates between Individuals with Traumatic and Nontraumatic Spinal Cord Injury
Bibliographic record
Abstract
Background: Musculoskeletal complications are one of the most common reasons for a patient with a spinal cord injury (SCI) to be rehospitalized. Bone loss due to immobilization and changes in metabolic processes because of the SCI lead to an increased risk of fractures. Objective: To evaluate the prevalence and demographic characteristics of people living with an SCI who had a secondary fracture. Methods: We used population health administrative data from Ontario, Canada, in individuals with either traumatic (TSCI) or nontraumatic SCI (NTSCI). Records of duplicate cases, missing unique patient identifier numbers, individuals not eligible for provincial health insurance, and age <18 years were excluded. Only records of fractures treated in the emergency department or acute care hospital were included. Descriptive statistics were used to summarize data, using counts and percentages that described the numbers and proportions of fractures by type disaggregated by sex, age groups, and type of SCI. Results: A total of 14,168 unique records were identified with 4486 as TSCI and 9682 as NTSCI between April 1, 2004 and March 31, 2020 and were followed up to March 31, 2021. Overall, 11% of the cohort had a subsequent fracture with no difference between TSCI and NTSCI. Hip fractures accounted for 21% of the fractures, wrists accounted for 12%, spine 11%, and tibia 11%. The average time to the first subsequent fracture after the SCI was 3.97 ( SD 3.4) years. Conclusion: Monitoring and management of fracture risk needs attention in the first 2 years, with a focus on NTSCI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".