Accuracy of Cut-off Acetabular Reamers for Minimally Invasive THA
Bibliographic record
Abstract
Cut-off reamers have been introduced for minimally invasive hip replacement to make reamer insertion through the small incision easier. However, the accuracy of cut-off reamers in comparison to traditional hemispherical reamers has not been documented. We reamed four human cadaveric hips using a cut-off reamer and three hips using a standard reamer. We started with smallest size reamer to remove subchondral bone, and the size was progressively increased until breaching the acetabular floor. We performed computed tomography scans for each reamer size to digitally determine the true dimensions and sphericity of the reamed acetabula. The cut-off reamers breached the acetabulum at a smaller size than with a standard reamer in two specimens, and at the same size as the standard reamer in one specimen. The accuracy of each reamer size was determined by quantifying the percentage of the reamed acetabular surface that was within 0.5 mm of the hemispherical reamer size. The average accuracy of the cut-off reamers was 70% compared with 81% for the standard reamers. The cut-off acetabular reamers showed a trend toward decreased accuracy that may be attributable to a tendency of the reamer to wobble in use.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".