Bibliographic record
Abstract
Canada’s growing population of older people with MS (PwMS) has warranted a closer look into factors associated with depression that may interfere with healthy aging. At the same time, researchers, clinicians, and medical professionals treating PwMS with mood disorders have spoken about the difficulty they face when having to determine a diagnosis of depression in this population. This difficulty arises because both MS and depression share various psychological and neurological symptoms (e.g., fatigue, pain, sleep difficulties, psychomotor retardation, and cognitive difficulties). It is also found that these overlapped symptoms vary when completing a self-report measure of depression, versus when medically diagnosed by a psychiatrist. As such, we aim to investigate the personal and disease-related factors that are associated with self-reported depressive symptoms and medically diagnosed depression (i.e. depression diagnosed by a medical professional). Following this, we aim to determine the risk factors for depression in older PwMS. This study used secondary data collected from the original study, the Canadian survey of health, lifestyle, and aging with multiple sclerosis. Data of the original study was collected from 743 Canadians (> 55 years of age and living with MS for >20 years). In this present study, presence of self-reported depressive symptoms was defined as a score of ≥ 8 on the depression component of the Hospital Anxiety and Depression Scale (HADS-D). Presence of medically diagnosed depression was determined by the item that asked participants if they have received a diagnosis of depression by their medical professional. Logistic regression was used to identify variables that predicted depression. Self-reported depressive symptoms were found in 30.5% of the population, while medically diagnosed depression was found in 25.7%. 11.7% of PwMS had both self-identified depressive symptoms and were diagnosed with depression by their medical professional. Low social support, high perceived disability, and additional comorbid physical conditions were independent predictors of depression in older PwMS in our cohort. Depression is prevalent in older PwMS. Clinicians should be cognizant of the overlap of symptoms between MS and depression and should employ possible ways to minimize over-diagnosing or underdiagnosing depression in this population. Identifying risk factors for depression is imperative because at-risk individuals may be thoroughly assessed for depression and will be able to receive treatment more promptly.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".