Morbidity and mortality following surgical treatment in periprosthetic femoral fractures. Is time to surgery the key?
Bibliographic record
Abstract
Objective. Total hip replacement is one of the most successful surgeries in recent years. The increase in total hip replacement procedures for the extension of indications, from primary/ secondary arthrosis to medial femoral neck fractures, is associated with an increase in the average age of the population leading to an inevitable increase in complications, such as periprosthetic proximal femoral fractures (PPFF), even if rare. However, the importance of this complication lies in the high mortality, poor outcomes, and often incomplete recovery. The objective of our study is to investigate whether time to surgery was a prognostic element in the treatment of these fractures, considering that these patients are often fragile and will require timely treatment like patients who fall within the native hip fracture protocol. Methods. 112 patients suffering from PPFF, classified with x ray according to Vancouver Classification were selected from 2007 to 2022 in two different hospitals in Rome. The minimum follow-up was 1 year and mortality, post-operative complications, and outcome were assessed in relation to time to surgery. Results. There was a significant correlation between mortality and time to surgery and each hour of delay to surgery the risk of mortality increased by 2%. Moreover, a statistical significance was found between the inability to restore walking ability in the early postoperative period and one year mortality. Conclusions. Patients with PPFF should be surgically treated with the same timing of patients with femoral neck fracture, in order to improve clinical outcomes, reduce post operative complications, restore walking ability, and reduce mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".