Utilizing Topological Clustering on a Traumatic Brain Injury Cohort: The Association of Neighborhood Socioeconomic Deprivation Profiles with Injury Mortality
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a significant contributor to global injury burden and a leading cause of injury mortality. While there is an established correlation between neighborhood socioeconomic deprivation and injury incidence, there is conflicting evidence of an association between neighborhood deprivation and injury mortality. We studied the association between neighborhood deprivation and key individual-level covariates with mortality in a provincial cohort of TBI patients. The primary study objective was to segment the population and explore differences in neighborhood deprivation among TBI patient clusters. A secondary objective was to determine the extent to which patient clusters were associated with injury mortality. TBI patients from 2014-2020 were sourced from the provincial trauma registry. Features included key individual-level factors as well as linked neighborhood deprivation. Topological Data Analysis (TDA) was used to identify the complex interactions between features, with respect to the underlying shape of the dataset, to generate TBI patient clusters to predict mortality. Cluster boundaries were defined using Louvain community detection. Differences between features were tested across clusters. There were 1922 patients included in the analysis. TDA was conducted using the Mapper algorithm with L2-norm projection and Isolation Forest as a 2-dimensional lens. Four distinct clusters were identified which showed differences across all features, indicating that this segmentation method effectively distinguished between patients with varying levels of neighborhood deprivation and mortality risks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".