Longitudinal healthcare utilization among traumatic spinal cord injury patients: a 20 year retrospective study using population-based data
Bibliographic record
Abstract
BACKGROUND: Patients with traumatic spinal cord injury (TSCI) experience the healthcare system in a heterogeneous fashion after initial injury. This study performs a retrospective analysis of administrative data to identify patterns of longitudinal healthcare utilization among patients with TSCI in British Columbia, Canada, up to 20 years after initial hospitalization. METHODS: Using population-based administrative databases, adult patients with incident TSCIs were identified between January 2001 and December 2021. Population-based healthcare administrative and demographic data were used to determine physician services (primary care and specialist), hospital admissions (elective surgical, medical, and emergency department), and clinical information. Descriptive summaries measuring healthcare utilization per person year were calculated. Average utilization calculated in person years since the time of injury was compared between those under 65 years of age and those who were over 65, and based on the level of injury (cervical vs. thoracic/lumbar). Latent-class analysis identified characteristics associated with high healthcare utilization. RESULTS: The cohort included 4132 patients with an incident TSCI. On average, the patients had 18.9 primary care provider (PCP) visits per person year after their injury occurred. Patients had 13.9 specialist visits per person year, of which the most common was with a neurologist. The average rate of hospital admission for all patients was 1.4 visits per year, and emergency department encounters occurred on average were 0.7 visits per year. Patients 65 and over and those with cervical injuries consistently utilized more healthcare resources compared to younger patients and those with thoracic/lumbar injuries (p < 0.001). Latent class modelling found that the highest healthcare utilization was among those with cervical spinal cord injuries and who lived in an urban area. CONCLUSIONS: Patients with TSCI had heterogeneous patterns of primary and specialist healthcare utilization up to 20 years after injury. Further analysis revealed that patients who had had cervical injuries and resided in urban centres accessed healthcare resources more frequently.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".