Prise en charge des fractures périprothétiques de la hanche : état actuel et perspectives; expérience du service de traumatologie et d’orthopédie CHU Mohammed VI de Marrakech
Bibliographic record
Abstract
INTRODUCTION: The increase in hip arthroplasties predicts a rise in periprosthetic fractures in Morocco, posing challenges for orthopedic surgeons. Therapeutic strategies vary considerably, highlighting the absence of a universally accepted treatment protocol. AIM: To analyze the management of per-prosthetic hip fractures, while addressing the challenges associated with them. METHODS: This was a retrospective study, conducted in the trauma-orthopedics department between December 2015 and November 2022. Nineteen patients who presented to the hospital with fractures around a hip prosthesis were included. RESULT: Nineteen periprosthetic fractures were observed. The majority of patients (68%) were women, with an average age of 68. The Vancouver classification showed that 52.6% of the fractures were type B1, and 21.1% type C, while the other fracture types were distributed differently. These fractures were mainly associated with diagnoses such as femoral neck fracture (63.2%) and coxarthrosis (31.6%). We observed variations in treatment recommendations and results between the different series analyzed. We noted discrepancies with certain series concerning fracture types and therapeutic choices. However, in our series, we achieved satisfactory results, with successful consolidation and the absence of complications in all patients. CONCLUSION: These results underline the importance of an individualized approach to fracture management, taking into account the specificities of each case.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".