The prediction of stress fractures using a ‘stressed volume’ concept
Bibliographic record
Abstract
This paper addresses an anomaly which exists in the current literature regarding stress fractures. Analysis of the data on fatigue strength of bone samples in vitro would conclude that these fractures should never occur at the strain levels known to occur in vivo. This anomaly can be resolved by including in the analysis the effect of stressed volume, whereby larger volumes of material are expected to have worse fatigue properties. A Weibull analysis was used to predict the probability of failure, Pf; this was an upper-bound prediction because it did not include the effects of remodelling and adaptation. Combining this analysis with a finite element model of the human tibia, we predicted a Pf value of 21% after five weeks of strenuous exercise, which is comparable with reported incidences in military personnel. The high incidence of stress fractures in the cannon bone of racehorses could also be predicted (Pf = 62%, compared to 70% experimentally). The approach can be used to investigate the effect of variables in the exercise regime such as the distance run per day and the use of improved footwear. It can also predict the increased risk of stress fractures in elderly people. The results suggest certain simple rules which may be of clinical value in designing exercise regimes and in understanding the risk factors for this type of injury.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".