Short Term Predictors of Unemployment in Multiple Sclerosis Patients
Bibliographic record
Abstract
BACKGROUND: Unemployment is common in people with multiple sclerosis (MS) and is associated with loss of income and impaired health related quality of life. This study determined variables associated with unemployment and risk factors for the development of unemployment in people with MS. METHODS: Ninety-six patients who were under age 65 and participated in two previous studies to measure economic costs and health related quality of life in MS were included. The baseline employment rate and variables associated with unemployment at baseline were determined. The ability of these variables to predict unemployment over the next two and a half years was then evaluated. RESULTS: At baseline 50.1% (50/96) of participants were employed. Two and a half years later only 40.6% (39/96) remained employed. This represents loss of employment for 22.0% (11/50) of those originally employed. Factors associated with unemployment at baseline included greater disability, progressive disease course, longer disease duration, and older age. Risk factors for loss of employment over the next 2.5 years included greater disability and older age. CONCLUSIONS: This study confirms the low employment rate among people with MS and confirms the association of several previously-reported factors with greater risk of unemployment. It is also the first study to confirm that some of these factors also increase the risk of future unemployment. People with MS who are over age 39 or have moderate disability and are still employed can now be identified as at risk for becoming unemployed over the next 2.5 years. They should be considered for interventions to maintain employment or to lessen the impact of unemployment.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".