Assessing clinical and patient reported outcomes of Sironix suture anchors in rotator cuff and Bankart repair surgeries
Bibliographic record
Abstract
BACKGROUND: Rotator cuff tears and Bankart lesions significantly affect shoulder function and quality of life. Arthroscopic rotator cuff repair and Bankart repair has become the standard treatment for restoring function and reducing pain. Recent advancements include new suture anchor technologies, such as the Sironix suture anchor known for its biomechanical strength and promising outcomes. However, there are limited real-world data on its effectiveness and safety, particularly in the Indian population. AIM: To evaluate the effectiveness and safety of Sironix suture anchors in rotator cuff and Bankart repair surgeries. METHODS: Sixty participants underwent surgery between January 2021 and December 2022, and demographic data and postoperative outcomes were collected through retrospective reviews and telephonic interviews. Validated scales, including the PENN Shoulder Score (PSS), Disabilities of the Arm, Shoulder, and Hand (DASH) score, and Single Assessment Numeric Evaluation (SANE), were utilized for assessment. RESULTS: Treatment with Sironix suture anchor devices, including Ceptre Knotted UHMWPE Suture Titanium Anchor, Spyke Knotted UHMWPE Suture Peek Anchor, Stativ Knotted UHMWPE Suture Anchor, and Viplok Knotless Peek Screw Anchor with Titanium Tip, revealed no repair failures. Participants demonstrated high satisfaction and functional improvement, as evidenced by the mean Quick DASH score (32.01) and PSS (71.65) and the satisfactory SANE scores for both injured joints (74.33) and non-injured (83.67) shoulder joints. CONCLUSION: The study yielded favorable outcomes for rotator cuff tear repair and Bankart repair. No repair failures were observed, supporting the safety and efficacy of these devices in shoulder injury management.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".