Should age be a factor in treatment choice of periprosthetic Vancouver B2-B3 proximal femur fractures? A retrospective analysis of mortality and functional outcomes in elderly patients.
Bibliographic record
Abstract
Background and aim of the work Revision Arthroplasty (RA) is considered the treatment of choice for periprosthetic femur fractures (PFF) presenting with a loose stem. In the elderly RA may be associated with high post-operative mortality and complications. The aim of this study is to compare mortality and functional outcomes of open reduction internal fixation (ORIF) and RA for B2-B3 PFF in the elderly. Methods The study population included 29 patients (>65 years) surgically treated for B2-B3 PFF at the Orthopedic and Traumatology Unit of Cattinara University Hospital in Trieste (Italy) between January 2015 and December 2019. 16 patients were treated with ORIF and 13 with RA. Mortality and functional outcomes were analyzed. Results In-hospital (6,25% vs 7,69%) and 3 months (6,25 vs 15,38%) mortality was higher in the RA group. Mortality rates were particularly high in the > 85-year-old patients within four months from RA treatment. One year (38,46% and 16,67%) and overall mortality (69,22% and 25%) was higher after ORIF. Average time to weight-bearing and ambulation was 2.6 and 5.25 months for ORIF patients and 1.3 and 2.4 months for RA. A correlation was found between delayed weight-bearing and overall mortality. Conclusions Age is a risk factor for short term mortality following RA. Patients >85 years of age could benefit from a less invasive procedure such as ORIF. Long term outcomes are generally better for patients who undergo RA but further studies are necessary to evaluate the risk-benefit ratio of RA treatment compared to ORIF in elderly patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".