Orthopedics in Space Travel: Developing Procedures to Evaluate the Safety of Implants Amidst the Rise of Commercial Space Tourism
Bibliographic record
Abstract
With the rise of commercial space tourism, the barrier of entry into space lowers. Therefore, passengers with more complex medical conditions are predicted to enter space. This report aims to initiate the development of procedures assessing the safety of space travel for individuals with orthopedic implants. In preparation for the 2023 sounding rocket launch by McGill Rocket Team, the Payload subteam developed a bone model, a human model, a finite element analysis model, and a testing model for determining the safety of orthopedic implants under the harsh conditions of spaceflight. Measuring the dynamic forces of the MRT's Portho's rocket in flight yielded vibrations in the 300-2750 Hz range, which is valuable for creating better models of the loading conditions on orthopedic implants in silico. Three point bending testing revealed high precision but low accuracy in measuring the mechanical strength of the models. Ultimately, the study recommends adjusting the human, bone, and testing models to prevent oversimplification. Further, future work should analyze bone screw interfaces on a microscopic level to detect small changes in implant stresses. By implementing these changes, procedures can accurately describe the safety of spaceflight for those with orthopedic implants.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".