Mechanical Testing of a Single Rod Versus a Double Rod in a Long-Segment Animal Model
Bibliographic record
Abstract
This study involved the mechanical testing of single-rod segmental hook fixation and double-rod segmental hook fixation in a long-segment animal model. The goals were first to compare the flexibility of a single-rod scoliosis construct with that of a double-rod construct when tested in torsion, and second, to determine the effect of not using instrumentation with every vertebral segment for the single rod. Another study found that the single-rod construct was as stiff in torsion as the standard double-rod construct in a model of 10 vertebral segments. The amount of neutral zone (NZ) rotation was tested in five calf spines using an MTS (Material Testing System) machine. Five constructs were tested and included 1) a single rod with hooks at every level except the apex; 2) a single rod with two fewer hooks; 3) a single rod with four fewer hooks; 4) a double-rod construct; and 5) no instrumentation. The amount of NZ rotation between vertebral segments was measured over 12, 10, 8, 6, 4, and 2 vertebral segments. An analysis of variance with all constructs showed that the instrumented spines had significantly less movement than did the uninstrumented spine. Statistical comparison using analysis of variance of constructs (constructs 1 to 4) showed that over 12 vertebral segments (T4-L3), all single-rod constructs (constructs 1 to 3) allowed more NZ rotation than did the standard double-rod construct. This testing indicated that over 12 vertebral segments the single rod allowed more NZ rotation than a double-rod construct.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".