Post Doc Competition (Knowledge Generation) ID 1986576
Bibliographic record
Abstract
Background Osteoporosis has been identified in individuals as young as 35 years of age post-spinal cord injury (SCI), with males seeming to be at a higher risk than females. However, it is not clear what the prevalence of fractures associated with SCI are in Ontario, Canada. Objective To determine the prevalence and demographic characteristics of fractures in people with a SCI. Methods Data will be sourced from administrative healthcare databases at ICES, Ontario. Inclusion criteria are people with SCI and at least one fracture since their SCI. We will exclude duplicate cases, records missing unique patient identifier numbers, and age <18 years. Descriptive statistics will be used to summarize sociodemographic and clinical data, disaggregated by gender, age groups, and associated injuries. Results We expect that younger adults would be more likely to have traumatic SCI and older adults to have non-traumatic SCI. We expect men to experience more traumatic SCI across all age categories. We expect that younger men will experience a higher incidence of secondary fractures as well as older women. Those with a longer time since initial SCI fracture are likely to experience a secondary fracture. Conclusion The proposed study is a foundational study to better understand fractures in people with a SCI. It is known that musculoskeletal complications are common post SCI, and osteoporosis is a common consequence of disuse due to SCI. It is necessary to understand the prevalence and impact of fractures post-SCI of those in Canada to develop prevention and management strategies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.976 | 0.913 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".