Nursing/Psychosocial/Health Services
Notice bibliographique
Résumé
1 Melanie A. Adams, 1 Bradley V. Vaughn, and 1 Megdad M. Zaatreh ( 1 Dept. of Neurology, Univ. of North Carolina School of Medicine, Chapel Hill, NC ) Rationale: Seizure control in epilepsy has been thought to be the primary goal for both patients and clinicians. However, practitioners may not be fully aware of what goals epilepsy patients truly wish to attain. We surveyed epilepsy patients in our clinic and asked them to list their primary goal for treatment of their epilepsy. We also considered how treatment regimen and seizure frequency may influence a patient's goals. Methods: We surveyed 48 patients in our Epilepsy Clinic at the University of North Carolina and asked them to list their primary goal for their epilepsy treatment, the number of antiepileptic drugs (AEDs) they used, and their seizure frequency. Results: Forty‐eight patients with a mean age of 41.3 years completed the survey and 32 were female. The subjects averaged taking 2 antiepileptic drugs and had an average seizure frequency of one seizure per month. We grouped the patients by number of AEDs used and seizure frequency. We found that patients who were using two or more antiepileptic drugs were more likely to list seizure control as their primary goal for treatment. Only 19% of patients taking one AED listed seizure control as their primary goal, but 75% of patients taking two AEDs listed seizure control as their goal and 93% of patients taking three AEDs listed seizure control as their goal. Additionally, patients that had a higher seizure frequency were also more likely to list seizure control as their primary goal for treatment. When looking at seizure frequency, 69% of patients who had more frequent seizures (seizures occurring at least once per month) listed seizure control as their primary goal for treatment. All of the patients (n = 4) who had more than one seizure per week, but less than one seizure per day, listed seizure control as their primary goal for treatment. Only 40% of patients who had been seizure‐free for one year or longer listed seizure control as their primary treatment goal. Conclusions: In our study we found that most of our epilepsy patients listed seizure control as their primary treatment goal. However, we found that patients who were taking more antiepileptic medications and had a higher seizure frequency were most likely to list seizure control as their primary goal. This may indicate that patients are willing to use more than one AED for seizure control despite the risk of an increase in side effects from the combination of medications. Additionally, we found that patients who were using one AED and had fewer seizures were least likely to list seizure control as their primary goal for treatment. This highlights the importance of communication between patients who have seizures and the practitioners who treat them to establish a treatment plan that can insure compliance from the patient, especially if the patient has refractory seizures. 1 Marlene Blackman, 1,2 Elaine Wirrell, and 1,2 Lorie Hamiwka ( 1 Pediatric Neurology, Alberta Children's Hospital, Calgary, AB, Canada ; and 2 Pediatrics and Clinical Neurosciences, University of Calgary, Calgary, AB, Canada ) Rationale: Children with epilepsy have greater behavior problems than healthy controls. Both intractability and mental handicap predict greater risk. Methods: Cohort study of 58 children aged 4–17 years with epilepsy attending a tertiary care pediatric neurology clinic. Parent‐completed Child Behavior Checklist (CBCL) subscores were compared between children with and without refractory epilepsy (defined as failure of >2 AEDs and seizures ≥ q3monthly over the past year) and with and without mental handicap using the Mann‐Whitney U test. Results: 58 children were surveyed, 21 with refractory epilepsy (4 normal cognition, 17 mental handicap) and 37 with non‐refractory epilepsy (22 normal cognition, 15 mental handicap). Groups did not differ significantly with respect to age or gender. Those with refractory epilepsy scored significantly higher (more abnormal) on the Social Problems (p < 0.05) and Attention Problems (p < 0.02) subscales of the CBCL. Those with mental handicap scored significantly higher on the Withdrawn (p < 0.001), Social Problems (p < 0.00001), Thought Problems (p < 0.00005), Attention Problems (p < 0.00001) and Aggressive Behavior (p < 0.02) subscales. Conclusions: While children with refractory epilepsy have greater behavior problems as indicated by the Social Problems and Attention Problems subscales, co‐morbid cognitive impairment appears to be even more predictive of poor behavioral outcome in children with epilepsy. 1 Janice M. Buelow, 1 Joan K. Austin, 1 Angela M. McNelis, and 1 Cheryl P. Shore ( 1 School of Nursing, Indiana University, Indianapolis, IN ) Rationale: Parents of children with epilepsy and Intellectual Disability (ID) report that their children have significant behavior problems. Past research shows that the level of family stress and child behavior problems are related, however the nature of this relationship is not well understood. In order to better understand the complex interaction of family stress and child behavior problems, this study describes the behavior problems of children with epilepsy and ID and parental perception of their child's behavior problems. Methods: Qualitative naturalistic inquiry was used to describe naturally occurring phenomena. Participants were 20 parents of children (9–16 years, mean = 12.2) who had at least two seizures a year or were on anti‐epilepsy medications, and had an IQ between 55 and 75. Open‐ended interviews were conducted with parents to explore the problems they experienced while raising their children. Interviews lasted about 1 hour, were tape recorded and transcribed verbatim. Each interview was analyzed for within‐case themes. After interviews were analyzed individually, themes were compared across cases to identify commonalities. Two researchers reviewed each interview and agreement was reached on themes Results: The most common problem behaviors were: (1) attention problems, (2) inappropriate behavior in public including temper tantrums, (3) poor socialization skills including inability to make friends, and (4) violent behavior including injuries to self and others. Parent themes regarding behavior were (1) I recognize and can manage my child's behavior, (2) my child's behaviors are just part of life, (3) my child's behaviors problems are because of others' actions and (4) my child's behavior problems are a result of seizures. Only one family out of 20 stated that there were no behavior problems. Conclusions: This study describes specific behavior problems that children experience and parental perceptions regarding the behavior. Child behaviors ranged from attention problems to violent behavior and family themes ranged from recognition of the problem to blaming the problem on outside events. Future studies should address the underlying causes of the behavior problems in these children with the goal of intervening to reduce or eliminate these problem areas. In addition, clinicians should be assessing for behavior problems in children with epilepsy and low IQ. (Supported by NR 04536 and NR 005035v 1 Kami D. Clark, 1 Steve S. Chung, and 1 David M. Treiman ( 1 Epilepsy Program, Barrow Neurological Institute, Phoenix, AZ ) Rationale: The number of medication and treatment options for the 2.3 million epilepsy patients in the U.S. in increasing. Understanding treatment regimens including medications and diagnostic testing is crucial for epilepsy patients. In addition, accurately conveying information concerning adverse effects of antiepileptic medications is critical. A lack of understanding or education may adversely effect medication compliance and treatment efficacy. Returning the phone calls of patients with questions is an important but often a time‐consuming and costly task for healthcare providers. We analyzed patient care related phone calls to try to identify ways in which patient education could be improved. Therefore, reducing the frequency of phone queries from patients. Met
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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,156 | 0,029 |
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 ».