Early offset‐increasing migration predicts later revision for humeral head resurfacing implants. A randomized controlled radiostereometry trial with 10‐year clinical follow‐up
Bibliographic record
Abstract
In a randomized controlled setting, medium-term implant migration and long-term clinical outcomes were compared for the Copeland and the Global C.A.P. humeral head resurfacing implants (HHRI). Thirty-two patients (mean age 63 years) were randomly allocated to a Copeland (n = 14) or Global C.A.P. (n = 18) HHRI. Patients were followed for 5 years with radiostereometry, Constant Shoulder Score, and the Western Ontario Osteoarthritis of the Shoulder Index (WOOS). WOOS and revision status were also obtained cross-sectionally at a mean 10-year follow-up. At the 5-year follow-up, total translation (TT) was 0.75 mm (95% confidence interval [CI]: 0.53-0.97) for the Copeland HHRIs and 1.15 mm (95% CI: 0.85-1.46) for the Global C.A.P. HHRIs (p = 0.04), but the clinical scores were similar at all follow-ups. The cumulative risks of revision at 5 and 10 years were 29% and 43% for Copeland and 35% and 41% for Global C.A.P HHRIs (p > 0.7). No implants were loose at revision, but HHRIs that were later revised followed an early offset-increasing migration pattern with medial translation and lift-off resulting in a mean 0.53 mm (95% CI: 0.18-0.88) higher TT at the 1-year follow-up compared to non-revised HHRIs. In conclusion, the Global C.A.P. HHRI had higher TT compared with the Copeland HHRI, but clinical scores and revision rates were similar. Nonetheless, revision rates were high and challenge the use of HHRIs. Interestingly, an early radiostereometry evaluated HHRI migration pattern with increased off-set predicted later implant revision.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".