Design and development of a computer assisted glenoid implantation technique for shoulder replacement surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Replacement of the diseased shoulder joint with implants is a procedure whose frequency is rapidly increasing. However, glenoid replacement remains challenging due to the difficult joint exposure and visualization of anatomical reference landmarks during the procedure. Improper positioning of the glenoid component can lead to early failure. The objective of this study was to develop and evaluate a Computer Assisted Glenoid Implantation (CAGI) technique to achieve a more accurate and reliable placement of the glenoid component. MATERIALS AND METHODS: Twenty cadaveric scapulae were imaged with CT. The accuracy of an electromagnetic tracking system and 3D surface modeling for the measurement of glenoid position was compared to that of the standard CT-based method. Custom jigs were then developed to track instruments and to correct for scapular motion during in vitro trials. A standardized protocol for determining, in real time, the glenoid position and placement was developed and validated. RESULTS: The version angles measured by the tracking system, CT, and the 3D modeling software were 0.0 +/- 1.2 degrees , -1.3 +/- 1.0 degrees , and -1.1 +/- 1.1 degrees , respectively. The magnitudes for inclination angles were 0.7 +/- 0.7 degrees , 0.9 +/- 0.8 degrees , and 1.0 +/- 0.7 degrees , respectively. A statistically significant difference was found only between measurements made with the tracking system and with CT (p < 0.05). Testing of the CAGI system in a cadaveric trial resulted in an accuracy of 1.17 degrees of version and 0.60 degrees of inclination. The procedure was readily performed with excellent feedback and guidance for the surgeon. CONCLUSIONS: Preoperative planning using CT imaging with 3D modeling and intraoperative tracking were combined to produce improved accuracy and reliability of glenoid implantation in the setting of total 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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it