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Record W1973477997 · doi:10.1002/jor.20250

Sex differences in long bone fatigue using a rat model

2006· article· en· W1973477997 on OpenAlexafffund
Luisa Moreno, Stephen D. Waldman, Marc D. Grynpas

Bibliographic record

VenueJournal of Orthopaedic Research® · 2006
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsQueen's UniversityLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsStrain (injury)Stress fracturesAthletesMedicineCreepMuscle fatigueFracture (geology)Physical therapyMaterials sciencePhysical medicine and rehabilitationComposite materialElectromyography

Abstract

fetched live from OpenAlex

Stress fractures can occur because of prolonged exercise and are associated with cyclic loading. Fatigue is the accumulated damage that results from cyclic loading and bone fatigue damage is of special concern for athletes and army recruits. Existing literature shows that the rates of stress fracture for female athletes and female army recruits are higher than their male counterparts. In this study, we used an ex vivo rat model to investigate the fatigue response of female and male bones. We determined the strain versus number of cycles to failure (S/N) for each sex and found that for a certain initial strain (5,000-7,000 microepsilon) female bones have shorter fatigue life. To further characterize the bone response to fatigue, we also determined the creep that occurred during the fatigue test. From the creep data, for a certain strain range, female bones accumulated greater residual strains and reached the critical strain at a faster rate. In summary, this study demonstrates that female rat bones have a lower resistance to fatigue in the absence of a physiological response such as muscle fatigue or osteogenic adaptation. From these results, we hypothesized that creep was the underlying mechanism that accounted for the fast deterioration of female bones during fatigue.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.325
GPT teacher head0.532
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2006
Admission routes2
Has abstractyes

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