Risk analysis and clinical outcomes of intraoperative periprosthetic fractures: a retrospective study of 481 bipolar hemiarthroplasties
Bibliographic record
Abstract
BACKGROUND: Intraoperative periprosthetic fractures (IPF) are a well-described complication following hip hemiarthroplasty. Our aims were to identify risk factors that characterize IPF and to investigate postoperative mobility. METHODS: We retrospectively reviewed 481 bipolar hemiarthroplasties for displaced femoral neck fractures; of which, 421 (87.5%) were performed without cement, from January 2013 to March 2018. Data on the patients' demographics, comorbidities, femoral canal geometry (Dorr canal type, Canal Flare Index), surgeon's experience (junior vs. senior surgeon), and timing of surgery (daytime vs. on-call duty) were obtained. In patients with intraoperative fractures, further information was obtained. Patient mobility was assessed using matched-pair analysis. Mobility was classified according to the NHFD mobility score. The chi-square test, Fisher's exact test, and Fisher-Freeman-Halton exact test were used for comparison between categorical variables, while the Mann-Whitney U test was used for continuous variables. The data analysis was performed using SPSS. RESULTS: Of 481 procedures, 34 (7.1%) IPFs were encountered. The Dorr canal type C was identified as a significant risk factor (p = .004). Other risk factors included female sex (OR 2.30, 95% CI .872-6.079), stovepipe femur (OR 1.749, 95% CI .823-3.713), junior surgeon (OR 1.204, 95% CI .596-2.432), and on-call-duty surgery (OR 1.471, 95% CI .711-3.046), although none showed a significant difference. Of 34 IPFs, 25 (73.5%) were classified as Vancouver type A. The treatment of choice was cerclage wiring. Within the 12 matched pairs identified, the postoperative mobility was slightly worse for the IPF group (delta = .41). CONCLUSIONS: IPF is a serious complication with bipolar hemiarthroplasty. The identification of risk factors preoperatively, in particular femur shape, is crucial and should be incorporated into the decision-making process.
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How this classification was reachedexpand
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".