Maximum load to failure and tensile displacement of an all-suture glenoid anchor compared with a screw-in glenoid anchor
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the biomechanical behavior of an all-suture glenoid anchor in comparison with a more conventional screw-in glenoid anchor, with regard to maximum load to failure and tensile displacement. METHODS: All mechanical testing was performed using an Instron ElectroPuls E1000 mechanical machine, with a 10 N pre-load and displacement rate of 10 mm/min. Force-displacement curves were generated, with calculation of maximum load, maximum displacement, displacement at 50 N and stiffness. Pretesting of handset Y-Knots in bone analog models revealed low force displacement below 60 N of force. Subsequently, three groups of anchors were tested for pull out strength in bovine bone and cadaver glenoid bone: a bioabsorbable screw-in anchor (Bio Mini-Revo, ConMed Linvatec), a handset all-suture anchor (Y-Knot, ConMed Linvatec) and a 60 N pre-tensioned all-suture anchor (Y-Knot). A total of 8 anchors from each group was tested in proximal tibia of bovine bone and human glenoids (age range 50-90). RESULTS: In bovine bone, the Bio Mini-Revo displayed greater maximum load to failure (206 ± 77 N) than both the handset (140 ± 51 N; P = 0.01) and the pre-tensioned Y-Knot (135 ± 46 N; P = 0.001); no significant difference was seen between the three anchor groups in glenoid bone. Compared to the screw-in anchors, the handset all-suture anchor displayed inferior fixation, early displacement and greater laxity in the bovine bone and cadaveric bone (P < 0.05). Pre-tensioning the all-suture anchor to 60 N eliminated this behavior in all bone models. CONCLUSIONS: Handset Y-Knots display low force anchor displacement, which is likely due to slippage in the pilot hole. Pre-tensioning the Y-Knot to 60 N eliminates this behavior. LEVEL OF EVIDENCE: I.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".