Outcomes of non-operatively managed Vancouver Type B1 periprosthetic femur fractures: a multi-center retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: This retrospective case series evaluated mortality outcomes in patients with Vancouver B1 periprosthetic fractures (PPFs) managed non-operatively using a matched cohort approach. We hypothesize that mortality rates will not significantly differ between operative and non-operative management of Vancouver B1 PPFs, as treatment decisions are likely driven by fracture complexity and patient comorbidities rather than a direct survival benefit of surgical intervention. METHODS: Thirty patients with Vancouver B1 PPFs managed non-operatively between 2011 and 2017 across five major Australian trauma centers were identified. Patients were propensity-matched to 60 operatively managed patients, matched by age, ASA score, length of stay, follow-up duration, and fracture sub-type (B1). Mortality rates at 30 days, 1 and 5 years were compared between the non-operative and operatively managed groups. For the non-operative group alone, the impact of weight-bearing status on mortality was assessed. RESULTS: There was no significant difference in mortality rates between the non-operative and operative cohorts at 30-day (3.3%; 1.7%; P = 1.00), 1 year (20.0%; 3.3%; P = 0.09) and 5 years (33.3%; 30.0%; P = 0.78). For the non-operative group alone, there was no significant difference in mortality rates between WBAT and non-WBAT groups at 30 days (7.7%; 0.0%; P = 0.400), 1 year (15.4%; 17.6%; P = 0.839) and 5 years (30.8%; 35.3%; P = 0.781), CONCLUSION: Comparable 5-year mortality rates were identified between non-operatively and operatively managed Vancouver Type B1 periprosthetic femoral fractures. Despite differences in age and comorbidities, non-operative management may be a viable option for selected patients, underscoring the need for further research to refine treatment guidelines. CLINICAL TRIAL NUMBER: Not applicable.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".