Can we predict the humerus stem component size required to achieve rotational stability in metaphyseal stability concept?
Bibliographic record
Abstract
Background: Implant manufacturers typically offer several sizes of a humeral stem for shoulder arthroplasty so that time zero fixation can be achieved with the optimal size. Stem size can be templated preoperatively but is definitively determined intraoperatively. The purpose of this study was to determine if preoperatively acquired parameters, including patient demographics and imaging, could be used to reliably predict intraoperative humeral stem size. Methods: A cohort of 290 patients that underwent shoulder arthroplasty (116 anatomic and 174 reverse) was analyzed to create a regression formula to predict intraoperative stem size. The initial cohort was separated into train and test groups (randomly selected 80% and 20%, respectively). Patient demographics, anatomical measurements, and statistical shape model parameters determined from a preoperative shoulder arthroplasty planning software program were used for multilinear regression. The implant used for all cases was a short-stemmed metaphyseal-fit prosthesis. Results: of 0.63 was obtained for the multilinear regression equation combining these parameters. When analyzing the cohort for the prediction of stem size in the test group, 95% were within plus or minus one size of that used during surgery. Conclusion: Preoperative criteria such as humeral geometry and proximal humeral bone density can be combined in a single multilinear equation to predict intraoperative humeral stem size within one size variation. Embedding the surgeon's decision-making process into an automated algorithm potentially allows this process to be applied across the surgical community. Predicting intraoperative decisions such as humeral stem size also has potential implications for the management of implant stocks for both manufacturers and health-care facilities.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".