Effect of the length of the superficial plate on bending stiffness, bending strength and strain distribution in stacked 2.0–2.7 veterinary cuttable plate constructs
Bibliographic record
Abstract
OBJECTIVES: Use of stacked veterinary cuttable plates (VCP) increases the construct stiffness, but it also increases the stress protection and concentrates the stress at the extremities of the implants. We hypothesized that by shortening the superficial plate, it would not reduce the stiffness of the construct, but that it would reduce the stress concentration at the plate ends. METHODS: A 3 mm fracture gap model was created with copolymer acetal rods, stacked 2.0-2.7 VCP and 2.7 screws. The constructs consisted of an 11-hole VCP bottom plate and a 5-, 7-, 9- or 11-hole VCP superficial plate. Five of each construct were randomly tested for failure in four-point bending and axial loading. Stiffness, load at yield, and area under the curve until contact (AUC) were measured. Strains were recorded during elastic deformation for each configuration. RESULTS: During both testing methods, stiffness, load at yield and AUC progressively decreased when decreasing the length of the superficial plate. No statistically significant differences were obtained for load at yield in four-point bending and AUC in axial loading. The strain within the implant over the gap increased as the length of the superficial plate decreased. CLINICAL SIGNIFICANCE: Shortening the superficial plate reduces the stiffness and strength of the construct, and decreases stress concentration at the implants ends. As the cross section of the implant covering the gap remained constant, friction between the plates may play a role in the mechanical properties of stacked VCP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".