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Enregistrement W4240037173 · doi:10.5435/00124635-201210000-00006

Sex Differences in Osteoarthritis of the Knee

2012· article· en· W4240037173 sur OpenAlexaff
Barbara D. Boyan, Laura L. Tosi, Richard D. Coutts, Roger M. Enoka, David A. Hart, Daniel P. Nicolella, Karen J. Berkley, Kathleen A. Sluka, K. Kwoh, Mary I. O’Connor, Wendy M. Kohrt

Notice bibliographique

RevueJournal of the American Academy of Orthopaedic Surgeons · 2012
Typearticle
Langueen
DomaineMedicine
ThématiqueOsteoarthritis Treatment and Mechanisms
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineOsteoarthritisIncidence (geometry)DiseaseGerontologyPopulationPhysical therapyQuality of life (healthcare)Ethnic groupDemographyInternal medicineAlternative medicinePathologyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Osteoarthritis (OA) is a leading cause of disability in the United States. It is the most common form of arthritis and afflicts 13.9% of adults aged ≥25 years and 33.6% (12.4 million) of those aged >65 years—an estimated 26.9 million persons in the United States.1 Studies sponsored by the Centers for Disease Control and Prevention and the National Institutes of Health have identified differences in the incidence and severity of OA between men and women, as well as between racial and ethnic groups.2,3 The burden of OA is highest among women and African-Americans, who disproportionately develop knee and hand but not hip OA. The disproportionate number of women in the aging US population is of clinical concern because of the more severe knee OA and its impact on quality of life and independence. Based on these factors, there is a need for research focused on the effect sex differences have in the development and progression of OA as well as the impact on prevention and treatment strategies. However, most studies on the mechanisms underlying OA have not taken sex differences into account, whether in vitro cell culture or animal models were used. Although little is known about the mechanisms that contribute to disparities between men and women in disease incidence and severity, they likely involve mechanical and molecular events in the affected joint. Diagnosis of knee OA is based on evidence of joint pain and/or reduced space between articulating bone surfaces as a result of thinning of the opposing articular cartilages. However, multiple tissues that compose the knee joint appear to be compromised by the disease, including subchondral bone, articular cartilage, the meniscus, the anterior cruciate ligament, the synovium, and synovial fluid. A change in any of these tissues can influence the distribution of load across the joint, with corresponding adaptations in the other tissues and, ultimately, the cartilages. Such pathophysiologic changes may exacerbate age-related physiologic changes in joint function attributable to genetic characteristics, age, sex, and health status, leading to greater cartilage damage. To understand the expression of knee OA in males and females, it is important to view the knee as an organ rather than focusing only on the articular cartilage. Knee tissues are modulated by sex hormones during tissue development and throughout the life cycle in both males and females. Although menopause is associated with an increase in OA severity in women, systemic estrogen alone cannot explain the observed sex differences. Recent data, for example, show that sex-specific variations in the responses of chondrocytes to sex steroids are the result of differences in receptor number as well as mechanisms of hormone action.4 In addition to increased prevalence of knee OA, women often have greater pain and more substantial reduction in function and quality of life than do men.5 OA pain can be related to the sensory information that emerges from the knee joint. The pain does not always match the degree of injury, however, and can continue even after total joint arthroplasty. The neural and other mechanisms underlying these differences in pain between men and women with knee OA are unknown. By improving our understanding of the mechanisms responsible for sex differences in the perception of pain in OA, more effective and, possibly, sex-specific treatment strategies will emerge. Although the adaptations that accompany advancing age may be a major factor in its etiology in older patients, early-onset OA is becoming more common. Women with physically active lifestyles, such as athletes and workers in occupations that involve exposure to traumatic injury or to mechanical stress, are more subject to early onset OA. Anterior cruciate ligament injuries are particularly problematic in 16- to 20-year-old females. Approximately 50% of these young women will progress to OA in 10 to 15 years.6,7 The prevalence of obesity in children and young adults is escalating, and the impact of increased mechanical stress on the knee during bone growth and development is not yet fully understood. In addition, the role of sex differences, particularly hormonal regulation, and its influence on the onset and progression of OA is not yet known. In summary, epidemiologic studies have established that sex differences exist in the incidence and severity of knee OA. Therapeutic approaches to the management of OA, particularly regenerative medicine strategies, have not yet taken these sex differences into consideration. Effective interventions, however, will require a better understanding of the mechanisms involved in the disease and its differential expression in men and women.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,026

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,274
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations24
Publié2012
Routes d'admission1
Résumé présentoui

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