Use of Ligament Advanced Reinforcement System tube in stabilization of proximal humeral endoprostheses
Bibliographic record
Abstract
AIM: To review outcomes following usage of the Ligament Advanced Reinforcement System (LARS(®)) in shoulder tumors. METHODS: Medical records of nineteen patients (19 shoulders) that underwent tumor excisional procedure and reconstruction with the LARS synthetic fabric, were retrospectively reviewed. RESULTS: Patients' median age was 58 years old, while the median length of resection was 110 mm (range 60-210 mm). Compared to immediate post-operative radiographs, the prosthesis mean end-point position migrated superiorly at a mean follow up period of 26 mo (P = 0.002). No statistical significant correlations between the prosthesis head size (P = 0.87); the implant stem body length (P = 0.949); and the length of resection (P = 0.125) with the position of the head, were found at last follow up. Two cases of radiological dislocation were noted but only one was clinically symptomatic. A minor superficial wound dehiscence, healed without surgery, occurred. There was no evidence of aseptic loosening either, and no prosthetic failure. CONCLUSION: LARS(®) use ensured stability of the shoulder following endoprosthetic reconstruction in most patients.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".