Computer Navigation and Patient-specific Instrumentation in Shoulder Arthroplasty
Bibliographic record
Abstract
Longevity of total anatomic and reversed shoulder arthroplasty largely depends on accurate correction of glenoid deformity and correct positioning and fixation of the glenoid component. However, the morphology of the scapula is inconsistent, varying degrees of osteoarthritis cause numerous anatomic changes, and standard 2-dimensional imaging and standard surgical instrumentation are imprecise for preoperative planning and execution of glenoid reconstruction. Recently, various authors have shown that preoperative 3-dimensional surgical planning and computer navigation technology may increase the accuracy and repeatability of the implantation of the glenoid component, especially for the position and orientation of the glenosphere and screws in reversed arthroplasty. These novel techniques may allow the surgeon to better define the preoperative deformity, select the optimal implant position, and then accurately execute the plan at the time of surgery. Future studies are needed to determine the long-term effect on functional outcome and cost-effectiveness of computer-assisted technology in shoulder arthroplasty.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".