Risk factors for the periprosthetic fracture after total hip arthroplasty: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND AIMS: A systematic review and meta-analysis was performed to investigate the risk factors associated with periprosthetic fracture after total hip arthroplasty. MATERIAL AND METHODS: We searched potential studies in the following databases: MEDLINE, Embase, Web of Science, SCOPUS and Cochrane CENTRAL up to December 2013. Newcastle-Ottawa Scale was used to evaluate the methodological quality, and Stata 11.0 was used to perform all the analyses. RESULTS: Seven studies altogether, including 1069 cases of periprosthetic fractures and 74,776 controls, were included in the meta-analysis. Compared to those absent following demographic or medical conditions, patients involved with female gender (odds ratio, 1.534; p < 0.001), advanced age (>80) (odds ratio: 4.203; p < 0.001), revision (odds ratio: 4.398; p < 0.001), rheumatoid arthritis (odds ratio: 2.503; p < 0.001), osteonecrosis (odds ratio: 1.563; p = 0.009), and implant type of Exeter (odds ratio: 1.511; p = 0.017) were more likely to sustain periprosthetic fractures. Osteoarthritis (vs not) (odds ratio: 0.449; p < 0.001) was identified a protective factor for periprosthetic fractures after total hip arthroplasty. The other factors, including lower ages, American Society of Anesthesiologists ≥ 3, and other implant types, were not significant risk factors for periprosthetic fractures. CONCLUSIONS: These medical conditions as reminder should be kept in clinicians' mind and close follow-up should be implemented in patients involved for preventing the occurrence of periprosthetic fractures after total hip arthroplasty.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it