[Analysis of the effect of surgical treatment of periprosthetic femoral fractures after hip replacement].
Bibliographic record
Abstract
OBJECTIVE: To analyze and compare the clinical efficacy of different types of surgical treatment of periprosthetic femoral fracture(PFF) after hip arthroplasty (HA). METHODS: From September 2010 to September 2016, 47 patients (47 hips) with periprosthetic fractures after total hip arthroplasty were retrospectively analyzed, including 13 males and 34 females. According to Vancouver classification, there were 2 patients with type AG, 17 patients with type B1, 19 patients with type B2, 7 patients with type B3 and 2 patients with type C. The age of patients ranged from 56 to 94 (71.5±8.3) years. After admission, nutritional risk screening (NRS2002) was used to assess the nutritionalstatus of the patients. Eighteen patients (38%) had malnutrition risk (NRS>3 points). After admission, the patients were given corresponding surgical treatment according to different types. Intraoperative blood loss was recorded. Harris score was used to evaluate the hip function. VAS pain score was performed on admission and after operation. RESULTS: <0.05). At the last follow-up, all the fractures were healed and the force line of lower limbs was good. No loosening, displacement, fracture of internal fixation, loosening and dislocation of prosthesis occurred during the follow-up period. CONCLUSION: The treatment of hip periprosthetic fracture patients should be based on the general situation of patients, imaging data, intraoperative correction classification, etc. to develop individualized treatment plan in line with patients. For patients with preoperative malnutrition risk, preoperative nutritional intervention may reduce intraoperative bleeding.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".