An examination of suicidal intent in patients with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To examine neurologic and psychiatric correlates of suicidal intent in a community sample of 140 patients with MS. METHODS: Patients with (28.6%) and without lifetime suicidal intent were compared across MS disease-related and psychiatric variables. All subjects were interviewed with 1) the Structured Clinical Interview for DSM-IV Axis 1 disorders (SCID-IV) to determine lifetime prevalence of major depression and anxiety disorders; and 2) the Social Stress and Support Interview to assess psychological stressors. Suicidal intent was documented with questions from the SCID-IV and Beck Suicide Scale. Patients also completed the Hospital Anxiety and Depression Scale and cognitive testing. RESULTS: Suicidal patients were significantly more likely to live alone, have a family history of mental illness, report more social stress, and have lifetime diagnoses of major depression, anxiety disorder, comorbid depression-anxiety disorder, and alcohol abuse disorder. By logistic regression analysis, the severity of major depression, alcohol abuse, and living alone had an 85% predictive accuracy for suicidal intent. A third of suicidal patients had not received psychological help. Two-thirds of subjects with current major depression, all suicidal, had not received antidepressant medication. CONCLUSIONS: Suicidal intent, a potential harbinger for suicide, is common in MS and is strongly associated with major depression, alcohol abuse, and social isolation. Suicidal intent is a potentially treatable cause of morbidity and mortality in MS.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".