Effect of Changing Indications and Techniques on Total Hip Resurfacing
Bibliographic record
Abstract
Recently, improved metal-on-metal bearing technology has led to the reemergence of resurfacing as a reasonable option for total hip arthroplasty. During the course of a prospective multicenter FDA-IDE evaluation of metal-on-metal total hip resurfacings, we modified our indications and emphasized surgical technique where the femoral surface area was small due to femoral cysts and small component size. We assessed the influence of these changes on complication rates in the first cohort of 292 patients and the second of 724, and then compared these outcomes in the second cohort with historical reports of resurfacing. We had a minimum followup of 24 months (mean, 33 months; range, 24-60 months). After changes were made in the indications and technique, the overall complication rate decreased from 13.4% to 2.1% with the femoral neck fracture rate reduced from 7.2% to 0.8%. The outcomes of the second cohort compare with modern-day resurfacing devices and appear superior to historical results. The data suggest patients should be carefully selected and technique optimized to reduce complications. Long-term followup is required to see if these promising results will be maintained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".