Probability of fatigue failure and minimum sample size requirements for cyclically loaded bone
Bibliographic record
Abstract
Fatigue-life measurements of bone exhibit a significant amount of scatter, which may be characterized probabilistically using a Weibull analysis. Despite an abundance of fatigue testing literature, a standard recommendation for the number of samples required to adequately characterize the probability of fatigue failure in bone does not exist. The primary objective of this work was to determine the minimum sample size required to fit a Weibull distribution to fatigue-life measurements of cyclically loaded bone. Two existing experimental datasets comprising cortical and subchondral bone samples were used in this work. Weibull parameters were estimated using both the maximum likelihood and rank regression methods. A Monte Carlo simulation was used to estimate Weibull parameters for different sample sizes and a convergence analysis was used to determine the minimum required sample size. A simulated dataset with known population parameters was also used to assess the accuracy of the estimated Weibull parameters and to compare the two estimation methods. Our findings suggest that as many as n = 11 samples may be required to adequately quantify Weibull parameters from fatigue tests of bone. At the converged sample size, Weibull parameters differed from true population-level parameters by 3 %-25 %, depending on the estimation method. The maximum likelihood method provided the most accurate and precise estimates of Weibull parameters. These findings provide a framework for future studies aimed at reliably quantifying the probability of fatigue failure in bone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".