Contributo de Condições Clínicas e de Estratégias de Regulação Emocional para a Sintomatologia Depressiva em Doentes com Neurodegeneração: um modelo preliminar
Bibliographic record
Abstract
Introdução: Doenças com condições clínicas neurodegenerativas (DCCNs) afetam pessoas com idades variadas, são crónicas, podem apresentar um curso progressivo e ser bastante debilitantes. A depressão tende a coocorrer com as DCCNs. Todavia, é escasso o conhecimento empírico sobre o efeito conjunto de condições clínicas decorrentes da neurodegeneração e de estratégias de regulação emocional disfuncionais na explicação da sintomatologia depressiva em doentes com neurodegeneração. Objetivos: Analisar um modelo preliminar, no qual a incapacidade global, dor neuropática, fadiga (condições clínicas resultantes da neurodegeneração), dificuldade em identificar sentimentos (alexitimia) e supressão de pensamento (estratégias de regulação emocional disfuncionais) explicam conjuntamente a sintomatologia depressiva em doentes com DCCNs. Método: Este estudo transversal incluiu 163 doentes diagnosticados com DCCNs e 189 indivíduos da população geral sem DCCNs, ambos sem outras patologias do foro neurológico. Foram administrados os seguintes instrumentos de autorresposta: um questionário sociodemográfico e clínico, Escala de Depressão da Depression, Anxiety and Stress Scales-21, World Health Organization Disability Assessment Schedule-12 itens, Escalas Analógicas Visuais do Pain Detect Questionnaire e da Fadiga, Toronto Alexithymia Scale-20, White Bear Suppression Inventory. Resultados: Os sintomas depressivos e a totalidade das suas potenciais variáveis explicativas diferenciaram-se estatisticamente entre os participantes com e sem DCCNs. Na amostra com DCCNs, as potenciais variáveis explicativas dos sintomas depressivos correlacionaram-se com estes e relevaram-se regressores significativos de tais sintomas em modelos de regressão linear simples. Os referidos resultados permitiram selecionar a dor neuropática, fadiga, incapacidade global, dificuldade em identificar sentimentos e supressão de pensamento como covariáveis do modelo de regressão linear múltipla. Neste modelo multivariado, fadiga, incapacidade global e dificuldades em identificar sentimentos (alexitimia) explicaram conjunta e significativamente 51.7% da variância da sintomatologia depressiva e os seus coeficientes de regressão estandardizados positivos indicaram que quando estas variáveis aumentaram, aumentaram os sintomas depressivos nos indivíduos com DCCNs. Discussão: Os resultados forneceram evidências preliminares para a clínica e para a investigação sobre a relevância de ser considerado o contributo conjunto de condições clínicas resultantes da neurodegeneração e de estratégias mal adaptativas de regulação emocional como variáveis que podem aumentar a vulnerabilidade à depressão em pessoas com DCCNs, concretamente, a fadiga, a incapacidade global e a dificuldades em identificar sentimentos relacionada com a alexitimia. Estas variáveis devem ser consideradas nos tratamentos da depressão nos referidos doentes. | Introduction: Diseases with neurodegenerative clinical conditions (DNCCs) affect people of varying ages, are chronic, can have a progressive course and can be very debilitating. Depression tends to co-occur with DNCCs. However, there is little empirical knowledge about the joint effect of clinical conditions resulting from neurodegeneration and dysfunctional emotional regulation strategies in explaining depressive symptoms in patients with neurodegeneration. Objectives: To analyze a preliminary model in which global disability, neuropathic pain, fatigue (clinical conditions resulting from neurodegeneration), difficulty in identifying feelings (alexithymia) and thought suppression (dysfunctional emotional regulation strategies) jointly explain depressive symptoms in patients with DNCCs. Method: This cross-sectional study included 163 patients diagnosed with DNCCs and 189 individuals from the general population without DNCCs, both without other neurological pathologies. The following self-administered instruments were administered: a sociodemographic and clinical questionnaire, Depression Scale, Anxiety and Stress Scales-21, World Health Organization Disability Assessment Schedule-12 items, Pain Detect Questionnaire and Fatigue Visual Analogue Scales, Toronto Alexithymia Scale-20, White Bear Suppression Inventory. Results: Depressive symptoms and all their potential explanatory variables differed statistically between participants with and without DNCCs. In the sample with DNCCs, the potential explanatory variables of depressive symptoms correlated with them and were significant regressors of such symptoms in simple linear regression models. These results made it possible to select neuropathic pain, fatigue, global disability, difficulty identifying feelings and thought suppression as covariates in the multiple linear regression model. In this multivariate model, fatigue, general disability and difficulty identifying feelings (alexithymia) jointly and significantly explained 51.7% of the variance in depressive symptomatology and their positive standardized regression coefficients indicated that when these variables increased, depressive symptoms increased in individuals with DNCCs. Discussion: The results provided preliminary evidence for the clinic and for research into the relevance of considering the joint contribution of clinical conditions resulting from neurodegeneration and maladaptive emotion regulation strategies as variables that can increase vulnerability to depression in people with DNCCs, specifically fatigue, general disability and difficulties in identifying feelings related to alexithymia. These variables should be considered when treating depression in these patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".