The effect of the combination of locking screws and non-locking screws on the torsional properties of a locking-plate construct
Bibliographic record
Abstract
Little is known about the torsional properties of bone-plate constructs when a combination of locking and non-locking screws have been used. Sixty cadaveric canine femurs were divided into three groups. In the first group, the plate was affixed using three non-locking screws. In the second group, only locking screws were used while a combination of one locking and two non-locking screws were used in the third group. All constructs were subjected to torsion until failure. Torque, angle of torsion, and work were all calculated at the maximum failure point, as well as at five degrees of plastic deformation, which was thought to be more representative of clinical failure. At the maximum failure point, the locking group had significantly higher torque, angle, and work values than the non-locking group. The combination group was intermediate to the two other groups, and significantly differed from the non-locking group in torque, and from the locking group in work. At five degrees of plastic deformation, the locking group required significantly higher torque and work than the non-locking group. The combination group required a significantly higher torque than the non-locking group. This study suggests that a construct composed of all locking screws will fail at a greater torque value, and sustain greater work to failure in torsion compared to a construct composed of all non-locking screws. The addition of a single locking screw to an otherwise non-locking construct will increase the torque at the offset failure point and may be of clinical value in constructs subjected to high torsional loads.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".