Trends in Hospitalization Associated with TBI in an Urban Level 1 Trauma Centre
Bibliographic record
Abstract
OBJECTIVE: Traumatic brain injury (TBI) is the single largest cause of death and disability following injury worldwide. The aim of this study was to determine the demographic, clinical, medical and accident related trends for patients with TBI hospitalized in an urban level 1 Trauma Centre. METHODS: Data were retrospectively collected on individuals (n = 5,642) who were admitted to the Traumatic Brain Injury Program of the McGill University Health Centre - Montreal General Hospital from 2000 to 2011. RESULTS: Regression analysis showed a significant upward trend in the yearly number of cases as well as an upward trending by year in the proportion of TBI cases aged 70-years-old or more. The Injury Severity Scale scores were positively associated with year indicating a slight increase in injury severity over the years and there was an increase in patient psychological, social and medical premorbid complexity. In addition, the Extended Glasgow Outcome Scale score tended to become more severe over the years. There was a slight decrease in the proportion of discharges home and in the proportion of deaths. CONCLUSIONS: These results will help to understand the impact of TBI in an urban Canadian level 1 Trauma Centre. This information should be used to develop public prevention strategies and to educate the community about the risk of TBI especially the risk of falls in the ageing population. These findings can also provide information to help health policy makers plan for future resources.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".