A review of patient matched implants for shoulder arthroplasty
Bibliographic record
Abstract
Background: Advances in technology have enabled implant designers and shoulder surgeons to strive towards improving implant survival and patient outcomes. Patient matched implants (PMIs) for arthroplasty have developed from the use of technologies such as computer aided design and computer-aided manufacturing technology and three-dimentional printing. Methods: We conducted a computerized search of the electronic databases. We included studies which reported on PMI used in shoulder arthroplasty. Data were extracted by authors, publication year, study level, study type, demographic data (age, sex, sample size), type of arthroplasty, follow-up time, and outcomes. Results: 5 studies were identified as being eligible for this analysis. 55 patients and 57 shoulders were included. The average age was 72.3 across 5 studies. Average follow-up was 28.26 months. 22/57 (39%) were for primary shoulder arthroplasty and 35/57 (61%) were revision procedures. 50/56 (89%) of shoulders improved. 7/56 (13%) of shoulder had a complication. Discussion: PMI for shoulder arthroplasty has so far only been used for severe glenoid bone loss in primary and revision shoulder arthroplasty. PMI can not only be used in the severe glenoid bone loss patient but there are some advantages to using it in the regular patient who attends seeking a shoulder arthroplasty. A limitation of our review is that there are no studies published on PMI for primary shoulder arthroplasty without significant glenoid bone loss. A paradigm shift in shoulder arthroplasty may occur where PMI is not only used for glenoid bone loss and challenging revision cases but also in primary shoulder arthroplasty without significant bone loss.
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 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) | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".