Biomechanical measurements of cortical screw stripping torque in human versus artificial femurs
Bibliographic record
Abstract
Femur fracture plates are applied using cortical bone screws. Surgeons do this manually by subjective 'feel' without monitoring torque. Few studies have quantified stripping torque in human bone. No studies have measured stripping torque in the artificial bones from Sawbones (Vashon, WA, USA) that are frequently used in biomechanical studies. The present aim was to measure stripping torque of cortical screws in human versus artificial femurs. Sixteen fresh-frozen human femurs and eight artificial femurs were used. Using a digital torque screwdriver, each femur had a 3.5-mm diameter uni-cortical screw manually inserted into the anterior midshaft until failure of the screw-bone interface. Results were normalized by cortical thickness and the screw-bone interfacial area. There were no statistical differences in human versus artificial data, respectively, for stripping torque (1741 +/- 442 N.mm, 2012 +/- 176 N.mm, p = 0.11), stripping torque/thickness (313 +/- 59 N, 305 +/- 30 N, p = 0.74), and stripping torque area (28.5 +/- 5.3 N/mm, 27.8 +/- 2.8 N/mm, p = 0.74). Artificial unicortical thickness (6.6 + 0.3 mm) was greater than human thickness (5.6 +/- 1.1 mm) (p = 0.02). For human specimens, there was a moderate linear correlation of absolute and normalized stripping torque versus standardized bone mineral density (R > or = 0.32) and clinical T-score (R = 0.29), but not with age (R < or = 0.29). Surgeons should be aware of the stripping torque limits for human femurs and potentially take steps to monitor these values during surgery. The artificial femurs being increasingly used in research accurately replicate human cortical properties during screw insertion. To date, this is the first series of human femurs evaluated for cortical screw stripping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".