Epidemiology of Long Bone Fractures in the Elderly and Treatment Outcome with Interlocking Nailing in Southwest of Nigeria
Bibliographic record
Abstract
BACKGROUND: According to the literature, long bone fractures are not common in the elderly, with fractures occurring as a result of low energy and trivial falls. While epidemiological studies of long bone fractures in elderly patients in developed countries are scarce, it is almost non-existent in resource-poor location. Hence, this study of the patterns and presentation of long bone fractures amongst the elderly population in an African poor resource setting and their short-term outcomes following operative intervention METHODOLOGY: This was a retrospective study involving 48 patients who were 60 years and above and had intramedullary nailing for their long bone fractures. Biodata and other variables of interest such as fracture aetiology, level, type, infection, union and further surgeries were extracted. Collected data were analyzed using the SPSS version 20. Statistical significance was inferred at p<0.05. RESULT: Forty-eight interlocking nailings done in the elderly patients who were 60 years old and above over a 15-year period (February 2004 -January 2019) were retrieved. The average age was 70.0 ± 7.51 years, with 56.3% as females. Closed fractures accounted for 75%, while the mechanism of injury was mostly Road Traffic Accident {RTA} (70.8%). Non-union was significantly related to the level of fracture, p = 0.04. While the infection rate was related to the type of fracture (open fractures), p = 0.02. CONCLUSION: Elderly long bone fractures followed majorly Road traffic accident (motorcycle-pedestrian) in resource-poor setting. for which most of the fractures united. The adverse outcome was associated with open fractures and proximal fractures.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".