Epidemiology and Clinical Outcomes of Acute Spine Trauma and Spinal Cord Injury: Experience From a Specialized Spine Trauma Center in Canada in Comparison With a Large National Registry
Bibliographic record
Abstract
BACKGROUND: Because relevant changes in the epidemiology of the traumatic spinal cord injury (SCI) has been reported, we sought to examine the demographics, injury characteristics, and clinical outcomes of patients with spine trauma who have been treated in our spine trauma center. METHODS: All consecutive patients with acute spine trauma who were admitted in our center from 1996 to 2007 were included. Comparisons among the four triennia were performed for demographics, injury characteristics, and clinical outcomes. Also, our 2001/2002 SCI data were compared with the National Trauma Registry (NTR) dataset. RESULTS: There were 569 patients (394 males, 175 females; ages from 15 to 102 years, mean age of 50 years) who were admitted with acute spine trauma. Although demographic profile has been steady over the last four triennia, the frequency of more severe spine trauma at the lumbosacral levels due to falls has increased overtime. The mean length of stay and in-hospital mortality rates have not significantly changed during the past 12 years. Our in-hospital mortality rate (4%) was significantly lower than the provincial rate from the Ontario Trauma Registry (7.5%; p = 0.005). Comparisons between our SCI data and the NTR dataset showed significant differences regarding age groups. CONCLUSIONS: Our results indicate that significant differences in the characteristics of acute spine trauma but not demographics have occurred overtime in our institution. Also, there were significant differences between our database and the NTR regarding age distribution. Our reduced in-hospital mortality rates in comparison with the provincial data reinforce the recommendations for early management of SCI patients in a spine trauma center.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| 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".