A comparison of trauma patients in urban and rural areas presenting to a Canadian tertiary care centre.
Bibliographic record
Abstract
BACKGROUND: The aim of our work was to examine differences between trauma patients in rural and urban areas who presented to a tertiary trauma centre in the province of Saskatchewan, Canada. METHODS: We identified a historical cohort of all level 1 trauma activations presenting to Royal University Hospital (RUH) from April 1, 2020, to March 31, 2022. We divided the cohort into 2 groups (urban and rural), according to the trauma location. The primary outcome of interest was 30-day mortality. Secondary outcomes of interest were hospital length of stay, readmission to hospital within 30 days of discharge, and complication rate. RESULTS: < 0.0007). CONCLUSION: Although we identified key differences in patient demographics, injury type, and injury severity, outcomes were largely similar between the urban and rural trauma groups. This finding contradicts comparable studies within Canada and the United States, a difference that may be attributable to the lack of inclusion of prehospital mortality in the rural trauma group. The longer length of stay in trauma patients from rural areas may be attributed to disposition challenges for patients who live remotely.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".