Use of pre-operative 3D planning software for revision shoulder arthroplasty: clinical experience data from a survey in a real-world setting
Bibliographic record
Abstract
Background: Preoperative 3D planning is routinely used in primary shoulder arthroplasty, while specific challenges in the revision setting make such approaches more cumbersome and less accessible. Recently, an established preoperative planning software (Blueprint; Stryker, Tornier SAS, Montbonnot-Saint-Martin, France) was expanded to offer a capability for planning of revision and complex primary shoulder arthroplasty cases. The aim of this study was to survey experienced surgeons on their perception of the new software feature for preoperative 3D planning in the setting of revision shoulder arthroplasty. Methods: An observational survey was conducted from January 2022 to October 2022 among orthopedic surgeons performing revision shoulder arthroplasty cases. The survey was part of the Early Product Surveillance program, with the primary goal of obtaining observational data from surgical experience in a real-world setting. A two-staged survey process was applied with separate questionnaires to seek voluntary feedback on the preoperative planning phase as well as on the intraoperative evaluation of the software planning features in revision shoulder arthroplasty. Results: Twenty-five fellowship-trained orthopedic surgeons from the USA and Canada participated in the survey and reported their feedback on 34 revision shoulder arthroplasty cases that were preoperatively planned with the use of Blueprint revision planning software. The surgeons were largely in favor of the revision software planning features and confirmed perceived benefits of its use in the preoperative planning stage of revision shoulder arthroplasty cases. Reported benefits in the preoperative planning phase included increased efficiency and improved ease of creating an appropriate surgical plan as well as increased confidence to execute revision shoulder arthroplasty cases. Surgeons also noted improvements in translation of preoperative planning to intraoperative execution of revision cases, including more appropriate implant selection and improved accuracy of implant placement. Conclusion: The feedback from fellowship-trained shoulder arthroplasty surgeons on the use of the new software feature for preoperative 3D planning of revision shoulder arthroplasty is largely favorable. Further research should be conducted to investigate whether these surgeon-perceived benefits can lead to improved clinical outcomes for patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".