Evaluation of the Proximal Ulna Dorsal Angulation for Ulnar Component Sizing in Elbow Prosthetic Reconstruction After Distal Humeral Resection of Tumor
Bibliographic record
Abstract
INTRODUCTION: Elbow prosthetic reconstruction after distal humeral tumor resection is challenging. We identify the value of the proximal ulna dorsal angulation (PUDA) as an easily-measured radiographic parameter that can help inform ulnar component sizing in the Solar Elbow System (SES) and the Modular Universal Tumor and Revision System (MUTARS), two modular prosthetic systems that are commonly used after tumor resection in this anatomic location. We hypothesized that a larger PUDA measurement would require smaller ulnar stems. METHODS: Demographic data and PUDA measurements were retrospectively reviewed for 514 patients. Multivariate regression was used to determine the effects of patient demographic data on the PUDA. PUDA measurements were collected by three independent reviewers on lateral elbow radiographs. MUTARS and SES templating software was then used to validate the relationship between the PUDA and ulnar stem sizing. RESULTS: Regression analysis showed no substantial contribution of demographic variables to the PUDA measurement (adjusted R2 = 0.02, F(6, 508) = 2.704, P = 0.01). The MUTARS implant fit 97% of elbows with a PUDA <5° and 91.6% of elbows with PUDA ≥5° (P = 0.26). The largest SES combination fit 100% of elbows with a PUDA ≤10° versus 93% of elbows with a PUDA >10° (P = 0.029). Elbows accommodating the largest SES combination had a smaller median PUDA (5.4° versus 11.7°, P = 0.034); elbows accommodating the MUTARS implant had a smaller median PUDA (5.4° versus 5.8°, P = 0.34). DISCUSSION: The PUDA is a valuable and easily used preoperative planning tool for prosthetic elbow reconstruction after tumor resection. The proximal ulna dorsal angulation can be easily measured to predict ulnar component fit and reduce intraoperative complications. In patients with a PUDA ≥5°, ulnar component stem fit for current systems may be more challenging.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".