Total shoulder arthroplasty with a second-generation tantalum trabecular metal-backed glenoid component
Bibliographic record
Abstract
AIMS: We evaluated clinical and radiographic outcomes of total shoulder arthroplasty (TSA) using the second-generation Trabecular Metal (TM) Glenoid component. The first generation component was withdrawn in 2005 after a series of failures were reported. Between 2009 and 2012, 40 consecutive patients with unilateral TSA using the second-generation component were enrolled in this clinical study. The mean age of the patients was 63.8 years (40 to 75) and the mean follow-up was 38 months (24 to 42). METHODS: Patients were evaluated using the Constant score (CS), the American Shoulder and Elbow Surgeons (ASES) score and routine radiographs. RESULTS: Significant differences were found between the pre- and post-operative CS (p = 0.003), ASES (p = 0.009) scores and CS subscores of pain (p < 0.001), strength (p < 0.001) and mobility items (p < 0.05). No glenoid or humeral components migrated. Posterior thinning of the keel and slight wear at the polyethylene-TM interface was observed in one patient but was asymptomatic. Radiolucent lines were found around three humeral (< 1.5 mm) and two glenoid components (< 1 mm) and all were asymptomatic. DISCUSSION: TSA with the second-generation TM Glenoid component results in satisfactory to excellent clinical performance, function, and subjective satisfaction at a mean follow-up of about three years. Radiographic changes were few and did not affect the outcome. TAKE HOME MESSAGE: This paper highlights that the second generation Trabecular Metal Glenoid has better outcomes than those reported with the first-generation component.
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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 | 0.001 |
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".