Gender Disparities in Neurobehavioral Symptoms and the Role of Post-Traumatic Symptoms in US Service Members Following Mild Traumatic Brain Injury
Notice bibliographique
Résumé
Women are more directly involved in combat operations today than ever before, currently making up 18.6% of officers and 16.8% of enlisted personnel in the United States military. However, women continue to be under-represented in military research. Studies that do consider gender differences in traumatic brain injury (TBI) outcomes have shown that women report significantly more post-concussive symptoms than men. Conclusions for true gender differences related to TBI are hard to make without controlling for non-TBI factors. The effects previously identified in the literature may be an artifact of how men and women differ in their response to injury, unrelated to the neurological recovery process associated with TBI. The objective of this study was to examine the effects of gender specifics on mild TBI (mTBI) sequelae on injured and uninjured control groups, and to investigate the role of post-traumatic stress disorder (PTSD) on symptom reporting. It should be noted that the terms “gender” and “men/women” are used in this article in place of “sex” or “males/females” given that we are not discussing biological attributes. A total of 966 United States military service members and veterans were included in the study. Of the total sample, 455 men and 46 women were in the mTBI group, 285 men and 31 women were in the injured controls group (IC), and 111 men and 38 women in the non-injured controls group (NIC). Post-concussive and quality of life symptoms were compared for men and women while controlling for combat exposure. MTBI and IC groups were also stratified by PTSD presentation. Measures used included the Neurobehavioral Symptom Inventory (NSI), PTSD Checklist (PCL-C), Traumatic Brain Injury Quality of Life (TBI-QOL), and Combat Exposure Scale. In the mTBI group, women had worse scores on NSI total, NSI Somatosensory and Affective clusters, and the TBI-QOL Anxiety, Fatigue, and Headache scales ( n 2 = 0.018–0.032, small to small-medium effect sizes). When PTSD was present, women had worse scores on the NSI Somatosensory cluster only ( n 2 = 0.029, small-medium effect size). In contrast, when PTSD was absent, women had worse scores than men on the NSI Somatosensory and Affective clusters, and the TBI-QOL Anxiety and Headache scales ( n 2 = 0.032–0.063, small to medium effect sizes). In the IC group, women had worse scores on the NSI Cognitive cluster and the TBI-QOL Fatigue and Pain Interference scales ( n 2 = 0.024–0.042, small to small-medium effect sizes). However, group differences were no longer found when stratified by PTSD sub-groups. In the NIC group, there were no significant group differences for any analyses. We were able to identify symptoms unique to women recovering from mTBI that were not present following other forms of physical injury or in healthy controls. However, the impact of PTSD exacerbates the symptom profile and its comorbidity with mTBI equates to most of the noted gender differences.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».