Does stem preheating have a beneficial effect on PMMA bulk porosity in cemented THA?
Bibliographic record
Abstract
In cemented total hip arthroplasty (THA), porosity plays a major role in the fatigue failure of bone cement. Stem preheating procedure is known to reduce the stem/cement interfacial porosity. In the literature, no information is available about the effect of such procedure on cement bulk porosity. This study helps to find out if stem preheating can have a beneficial effect on bulk porosity, thus enhancing long-term bone cement integrity. A simplified experimental model of a stem/cement/bone construct of a cemented THA is designed to reproduce the mechanical boundary conditions of polymerizing cement. Effect of stem preheating and polymethylmethacrylate prechilling and mixing method (hand mixed and vacuum mixed) on cement porosity are investigated. Bulk porosity is analysed within three zones across the cement mantle in terms of pore number, pore area, and mean pore size. The results demonstrate that bulk cement porosity is strongly influenced by stem preheating, cement precooling as well as cement composition and mixing method. Stem preheating procedure displaces the porosity away from stem/cement interface toward bone; consequently reducing the pore area within the zone near the stem and increasing it in the middle and bone/cement zone. The most pronounced beneficial effect of stem preheating before implantation is visible for vacuum mixed procedure as the cement contains few pores of very small size (<100 μm). However, if stem is preheated, cement precooling should be avoided as it could counteract the beneficial effect of reduced porosity inside cement mantle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".