Pyrocarbon in shoulder hemi arthroplasty
Bibliographic record
Abstract
Pyrolytic carbon, or pyrocarbon (PyC), has biocompatibility, a low friction coefficient and good wear resistance, making it a material of great potential for use in orthopaedics. PyC shoulder implants have shown promising mid-term results, but there is a shortage of research that support expectations of reduced glenoid wear in shoulder hemi arthroplasty (HA), and a lack of randomised trials comparing PyC to other options of shoulder arthroplasty. \n \n \n \nThis thesis synthesizes findings from four studies focusing on shoulder arthroplasty outcomes and PyC, using data from the Swedish Shoulder Arthroplasty Registry (SSAR), and clinical trials. The studies investigate various implants and describe patient-reported outcome measures (PROMs), implant stability, and revision rates across different arthroplasty types. \n \n \n \nFor study 1 we utilized data on 1140 shoulders from the SSAR to compare the results after resurfacing hemi arthroplasty (HA) and stemmed HA. Younger patients were shown to have higher revision rate irrespective of implant type. Patient-reported outcome was better for patients with primary osteoarthritis (OA) when compared to patients with secondary OA. \n \n \n \nIn study 2 we evaluated the reliability, validity, and responsiveness of the Swedish translation of the Western Ontario Osteoarthritis of the Shoulder index (WOOS), affirming its suitability for assessing clinical outcomes in shoulder arthroplasty patients. We could also show that WOOS is a stable and consistent tool for longitudinal outcome measurement. \n \n \n \nStudy 3 compares the performance of pyrocarbon (PyC) versus CobaltChromium (CoCr) resurfacing implants in a randomised controlled trial (RCT). Findings suggest that PyC implants may offer advantages in terms of reduced glenoid erosion and lower risk of revision compared to CoCr implants, although larger studies are needed to confirm these results. \n \n \n \nIn study 4, extracting data from SSAR, we analysed results after stemmed PyC HA (n=101) and compared them to results after total shoulder arthroplasty (TSA) (n=142). We noted comparable hazard ratios (HR) for revision when adjusted for confounding factors. The results from PROMs for TSA were superior to PyC HA. \n \n \n \nIn conclusion, PyC HA appears to be a safe alternative in shoulder arthroplasty surgery. Our results show comparable or better outcomes than other options for HA. In comparison to TSA, PyC HA have lower outcome in PROMs but similar risk of revision. The patient demographics differ between the groups, clouding interpretation of outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".