Demographic and population-based analysis of traumatic injury transport outcomes and health-care infrastructure in Northern Québec's rural communities
Bibliographic record
Abstract
Introduction: North American and international studies have shown mortality and morbidity rates from traumatic injury to be higher in remote and rural populations when compared to urban areas. Little research is explores the available health infrastructure and outcomes of traumatic injuries in such regions, which include Northern regions of Canada (especially British Columbia and Québec), the rural outback of Australia, remote regions in Norway, and very isolated areas in the U.S, amongst others. In isolated Northern Québec communities, transport to the McGill University Health Centre (MUHC), a level-I regional trauma centre is the only option for complex trauma care. This study aims to provide: (1) a demographic analysis of the Northern Québec region, with an emphasis on characterizing the available health care infrastructure; (2) the mechanisms and rates of injuries in the North that require transfer; (3) transfer times and outcomes in patients with traumatic injury from this region.Methods: A manuscript focusing on trauma patient transport outcomes from Northern Québec is incorporated into this thesis. For this portion, quantitative data from trauma cases transferred to MUHC from Northern Québec was obtained from the MUHC trauma registry (Jan 1, 2005 to Dec 31, 2009). Demographic and health services data was obtained from the Reseau universaitaire integre de santé de l'Universite McGill (RUIS), the administrative coordinator of health and trauma services in Northern Québec. We identified mechanisms of injury, transfer times, and survival results in trauma patients transported to the MUHC from Northern Québec and compared the results to a population of trauma patients transported from Montreal's local suburban hospitals.Results: Pertinent literature was identified and summarized to provide an overview of rural and remote trauma experience. Emphasis was placed on ecologic analysis of rural trauma outcomes, use of geographic mapping systems and other scores to quantify remoteness, and a descriptive comparison of rural trauma experiences in Canada, Australia, and Norway. Assessment of the Northern Québec trauma experience revealed that the MGH received 9952 traumas during the study period. 254 of these patients were from the North and had an ISS above 15. 1027 patients with an ISS above 15 were transported from local suburban hospitals. The mean age for the local transport groups was > 40 years and form the North it was > 30. Both groups had a predominantly male population, the majority of whom had sustained blunt trauma. Motor Vehicle Collision was the most common mechanism in the Northern Québec population, averaging 40%. Penetrating trauma was the cause of 21.7% of all transports from Northern Québec, whereas it represented 12.5% of the injuries seen in the local transport population. Patients transferred from the Northern region with an ISS > 15 had a significantly higher mortality rate.Conclusion: Despite the selection and referral biases inherent in observational data of this type, the higher mortality rate observed in patients transferred from Northern Québec likely reflects challenges in timely transport and advanced care. Improved outcomes may result from enhanced/systematic training of local care providers, improved triage and rapid transport protocols.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".