Bibliographic record
Abstract
STUDY DESIGN: A mixed cross-sectional survey and cohort study using a prospectively gathered database of persons with traumatic spine injury. OBJECTIVES: To identify demographic and injury mechanism factors that predict greater injury severity, and to determine the effect of injury severity on outcomes in traumatic spine fracture. SUMMARY OF BACKGROUND DATA: Traumatic spine fracture outcome studies have focused on defining type and level of vertebral fracture without considering the severity of associated injuries. In the trauma population, greater injury severity has been shown to be related to worse outcome. No studies have been reported on the effect of injury severity on outcome in the traumatic spine fracture population. METHODS: Prospectively collected data on 830 persons with traumatic spine injury who were admitted to a trauma hospital were reviewed. Patient demographics; injury mechanism; hospital events; and disability, employment, and pain status at discharge, 1 year, and 2 years after injury were recorded. Associations between these factors and trauma severity (Injury Severity Score) were explored using Pearson's correlation and analysis of variance. RESULTS: Trauma was more severe in patients who had been married previously, who were involved in a motor vehicle accident, were ejected from the vehicle, had loss of consciousness, had higher-level and multiple complicated vertebral fractures, or had neurologic deficit. Those more severely injured had longer lengths of stay, more surgery, more complications, higher mortality, more disability, and less return to work. CONCLUSIONS: Persons with traumatic spine injury and polytrauma have poorer short- and long-term outcomes. This high-risk group may require aggressive interventions, more hospital resources, and close follow-up observation after discharge from hospital to optimize outcome.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".