Quality of Reamed Surface Using Serrated Blades as Compared to a Conventional Acetabular Reamer
Bibliographic record
Abstract
Background: Hip arthroplasty requires the preparation of the acetabular cavity to allow a proper contact between the bone and the implant. It is essential to allow osseointegration and long-term stability of the implant. The aim of this study was to conduct experimental testing to evaluate the quality of reamed surfaces using a serrated blade acetabular reamer as compared to a conventional rasp reamer. Method of Approach: Reaming tests were performed on a computerized numerical control tools machine at a rotational speed of 250 rpm and 3 different penetration speeds: 0.20 mm/s, 0.55 mm/s and 0.90 mm/s. For each reamer, a complete semi-hemispherical hole was perforated in 7 polyurethane samples. The reamed surfaces were digitized with a three-dimensional high resolution (40 μm) self-positioning laser scanner to carry out a quantitative analysis of the surface quality. Results: Results demonstrated that the cutting edge and the penetration speed influence the quality of the reamed surface. The serrated blade was found to reduce surface irregularities (ranging between 0.19 mm to 0.21 mm for the conventional rasp and between 0.07 mm and 0.12 mm for the serrated blades), and to reduce inaccuracies on the reamed cavity diameter (0.13 mm ± 0.05mm for the rasps and 0.06 mm ± 0.03mm for the serrated blade). Conclusions: The use of such tool by the surgeon may influence the stability of the acetabular implant and reduce the risks of revision surgery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".