Experimental Parametric Study of the Factors Leading to Elevated Femoral Intramedullary Pressure and Fat Embolus Syndrome in Orthopaedic Procedures
Bibliographic record
Abstract
During orthopaedic procedures such as total knee arthroplasty (TKA), total hip arthroplasty (THA), and intramedullary nailing, it is necessary to hammer implants into the intramedullary canal of long bones. This hammering action can generate a high intramedullary pressure, leading to the release of bone marrow fat globules into the cardiovascular system, and ultimately, the possible development of fat embolism syndrome. In the present study, the effect of parameters such as implant tip geometry, peak impact force, hammer tip material, bone to implant radial gap, and marrow viscosity, on the resulting intramedullary pressure generated when hammering implants into a simulated femur analogue was examined. The bone analogue consisted of a porous plastic cylinder, having similar porosity and pore size to human femoral bone, with bone marrow being represented by a paraffin wax/petroleum jelly mixture. It was found that intramedullary pressure is only slightly lowered by a change in implant tip geometry, and that the use of a steel tipped (as opposed to rubber) hammer resulted in an increase in average pressure in the proximal portion of the bone, but a decrease distally. A lower implant insertion speed, lower hammering force, and a larger bone to implant radial gap were found to significantly reduce the intramedullary pressure. The number of hammer strikes required to insert an implant was found to increase significantly with marrow viscosity, but the average intramedullary pressure was found to decrease with increasing viscosity. Numerical modelling was also found to offer great promise for analysing hammering procedures for orthopaedic research into fat embolism syndrome. Numerical and experimental results were matched with approximately a 20% deviation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".