Sex differences in the risk of heart disease among individuals with spinal cord injury
Bibliographic record
Abstract
Spinal cord injury (SCI) is a devastating neurological condition for which there is not yet a cure. In the SCI population, the sex distribution is disproportional with a male-to-female ratio of approximately 3:1. In addition to motor paralysis and sensory dysfunction, SCI causes significant autonomic dysregulation of the heart and vasculature. There is an increased risk of cardiovascular disease (CVD) among individuals with SCI compared with the general population, though there is a paucity of evidence on sex differences. Utilizing three databases, this thesis contributes to the field of knowledge by examining sex differences in the risk of heart disease among individuals with SCI and examining if sex differences are amplified by SCI, lesion level, or completeness of injury. First, population-level data from the cross-sectional Canadian Community Health Survey were examined through multivariable logistic regression models. Among the SCI population, male sex conferred a significantly increased odds of heart disease than female sex. Relative to the general population, SCI significantly amplified the sex-related differences in heart disease. Second, sex differences in the prevalence of recent heart disease among individuals with SCI, and its association with lesion level and completeness, were examined, utilizing the cross-sectional SCI Community Survey. Among this SCI population, male sex conferred a significantly increased odds of heart disease than female sex, though the association was not significant. Further, no significant sex-by-lesion level or sex-by-completeness interactions were found. Third, inpatient readmissions data from the Nationwide Readmissions Database with a prospective 1-year follow-up period were examined through survival analysis. Among patients admitted for SCI, male sex conferred an increased risk of ischemic heart disease readmission after adjusting for age and potential confounders, though the difference was not significant. Overall, there appears to be an association between sex and heart disease among individuals with SCI. These findings offer insight into the knowledge gap concerning sex-specific risk estimates of heart disease among individuals with SCI. Specifically, these results may reveal the need for sex-specific targeted CVD prevention strategies and may also inform a better understanding of CVD progression in both individuals with SCI and the general population.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".