Long-term outcomes of major trauma with unstable open pelvic fractures: A population-based cohort study
Bibliographic record
Abstract
Aims The aims of this study were to describe the profile and longer-term outcomes of major trauma patients with unstable open pelvic fractures. Patients and methods An observational study was performed using data from the population-based Victorian State Trauma Registry. Adult (≥16 years) major trauma patients who had sustained an unstable open pelvic fracture between 1 July 2010 and 30 June 2017 in Victoria (Australia) were included. Patient demographics, injury event, severity and management data were extracted. Patients were followed up at 6, 12 and 24 months post-injury to collect information about health status, function and return to work. Results There were 67 patients. The mean (SD) age was 41.4 (18.3) years, and 66% were male. Seventy-six per cent were road traffic injuries, 96% were managed at Level 1 trauma centres and all were multi-trauma patients. A third were Tile C fractures and 80% underwent surgical stabilisation of the pelvic injury. Eighty per cent were admitted to intensive care. The in-hospital mortality rate was 9%. Most (89%) survivors were discharged to an inpatient rehabilitation facility. The proportion classified as ‘severe disability’ on the Glasgow Outcome-Scale Extended declined from 38% at 6 months to 19% at 24 months. The overall three-level EuroQoL five-dimensional instrument score increased with time indicating better health status, and 50% of patients returned to work by 24 months. Conclusions Major trauma with unstable, open pelvic fracture is rare. Low in-hospital mortality was observed. Most survived to hospital discharge and outcomes improved with time post-injury, but 75% of patients experienced persistent pain and ongoing mobility and activity restrictions.
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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 | 0.001 |
| 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 | 0.000 | 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".