Sex Bias Is Increasingly Prevalent in Preclinical Cardiovascular Research: Implications for Translational Medicine and Health Equity for Women
Notice bibliographique
Résumé
nsuring that women are adequately represented in clinical trials is recognized as essential for sex equity in health.However, the use of female animal models and sex-based reporting have not been equally enforced in preclinical stages of research, which often precede and inform clinical trials.In 2014, the National Institutes of Health announced that it would require that sex be considered as a biological variable in applications for preclinical research funding, 1 yet a reluctance to include female animal models in preclinical experiments persists.Inappropriately inferring experimental findings to both sexes when a single sex is studied or when sex is not specified has the potential to disadvantage women by skewing our understanding of disease processes toward male-predominant patterns and by reducing the likelihood of female-specific therapeutics advancing to the clinical realm.We therefore systematically examined all preclinical cardiovascular studies published in American Heart Association journals with archives spanning at least 10 years (Circulation, Circulation Research, Hypertension, Stroke, and Arteriosclerosis, Thrombosis, and Vascular Biology [ATVB]) for evidence of sex bias.Full articles published between July 2006 and June 2016 and reporting original data from in vivo experiments in nonhuman mammals on pathophysiology, genetics, or therapeutic interventions directly relevant to a specific cardiovascular disorder in humans were included.Studies on physiological or genetic characteristics were included if they proposed potential therapeutic applications or implications of the study findings.Each journal article was independently reviewed and prespecified data were extracted by using standardized case report forms by 2 authors, including the cardiovascular disease investigated, animal model(s) used and their sex, whether study samples were sex matched (in studies using both sexes), whether at least 1 study result was reported by sex, and whether the use of a single sex was reported as a limitation (in single-sex studies).Interrater agreement was calculated using the Cohen κ-statistic and percentage agreement.Discrepancies were resolved by consensus or independent adjudication.Categorical variables were compared via χ 2 tests.Temporal patterns were evaluated via Pearson correlation or Cochrane-Armitage trend tests.All analyses were performed by using SAS 9.4 (SAS Institute Inc) with the use of an α-level of 0.05 to define statistical significance.Of 28 636 articles screened, 3396 met inclusion criteria and were analyzed.Interrater agreement for study inclusion before resolution was 94.5% (κ=0.72;95% confidence interval, 0.70-0.73).The sex of the animals used was not reported in 20.0% of studies.Males were exclusively used in 71.6% of studies in which sex was reported, whereas females were exclusively used in 12.9% and both sexes in 15.5%.Sex matching of animals was reported in 17.1% of studies that included both sexes.Restricting this analysis to the 988 studies of therapeutic interventions did not appreciably change these distributions.When stratified by the cardiovascular disease studied, males were exclusively used significantly more often than females or both sexes in all cases, with the exception of atherosclerosis and arrhythmia.When stratified by the animal model
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,029 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».