[The risk factors of periprosthetic fracture after hip arthroplasty:a meta-analysis].
Bibliographic record
Abstract
OBJECTIVE: To explore risk factors of the periprosthetic fracture after hip arthroplasty. METHODS: Potential studies were searched in databases including Pubmed, Embase, Cochrane Library, CNKI as well as Wanfang Database up to November 2018 and references in related literatures. The methodological quality of literature was estimated by Newcastle-Ottawa Scale. Raw data were merged and tested mainly by Revmain 5.3. RESULTS: <0.01) were less likely to suffer periprosthetic fracture after hip arthroplasty. Other factors were not significantly relevant to periprosthetic fracture after hip arthroplasty, including the age, preoperative diagnosis(femoral head necrosis, osteoarthritis, developmental dysplasia of the hip, femoral fracture, concomitant heart diseases) and American Society of Anesthesiologists >=3. CONCLUSIONS: Orthopedics doctors should constantly be cantious about the risk factors including female, revision and diagnosis of rheumatoid arthritis. They are supposed to prevent the periprosthetic fracture by gentle operation during hip arthroplasty and monitoring the functional exercise after operations when the above risk factors occur.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.027 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".