Thermal cycling can extend tool life in orthopaedic operating rooms
Bibliographic record
Abstract
Thermal cycling is a temperature modulation process developed to improve the performance, durability and longevity of materials. This process has been successfully utilized in the automotive, aeronautic and manufacturing industries. Surgical cutting tools undergo cyclical loading and generally fail by dulling, suggesting that thermal cycling may improve their performance and longevity. Ten 2.5 mm orthopaedic drill bits were randomized, with five undergoing thermal cycling within their sterile packaging and five serving as untreated controls. Using a servohydraulic testing machine, 100 drilling cycles were performed with each drill bit into the diaphyseal region of bovine femurs. After every 25 cycles, data was collected by performing identical drilling cycles into simulated human cortical bone material. Maximum force, maximum normalized torque and drilling work were measured, and a scanning electron microscope was used to measure outer corner wear. After 100 drilling cycles, the maximum drilling force, maximum normalized torque, drilling work and microscopic outer corner wear were all significantly lower for the treated drill bits (p < 0.05). Thermal cycling has the potential to decrease operating room costs and thermal necrosis associated with dull cutting tools. Application of this technology may also be relevant to surgical cutting tools such as saw blades, burrs and reamers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".