Low-Energy Pelvic Ring Fractures in the Elderly Population: Expected Outcomes and Associated Mortality Rates
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to uncover mortality risk utilizing a retrospective review at a level I trauma center in addition to demographic factors. METHODS: Patients aged 65 and older with low-energy closed pelvic ring fractures treated non-operatively from 2007 to 2017 were queried from the level I trauma center database. Mortality rate and associated risks were calculated. RESULTS: The average age of all the patients included in this study who sustained a low-energy pelvic fracture was 83.1 years (± 7.5; 66 - 97). The mean length of stay was 4.6 days (± 4.4; 0 - 37). The mean number of comorbidities was 2.2. The 1-year mortality rate was 23%. The relative risk (RR) of 1-year mortality for low-energy pelvic fractures for ages 65+ did not statistically differ compared to the US population in 2016 (6.6%) (RR: 1.0; 95% CI). The 2+ comorbidities showed a statistical significance in the pelvic fracture population with a P value of 0.037. Race, sex, discharge disposition and length of stay did not reach statistical significance (P > 0.05). CONCLUSION: Low-energy pelvic injuries do not appear to increase rate of mortality compared to the US population. Fracture pattern, race, sex, discharge disposition and length of stay do not seem to have an effect on mortality. Elderly patients with an average age of 84.5 years and more than two comorbidities had higher rates of mortality; however, these patients were likely to sustain earlier mortality regardless of low-energy pelvic fracture.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".