Prevalencia e impacto de las comorbilidades en pacientes con esclerosis múltiple
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis is a chronic, inflammatory and degenerative disease of the central nervous system. In most cases it is characterised by the recurring focal neurological deficit, which may become progressive over time. Given the chronic nature of the disease, patients may present with additional diseases (comorbidities), which affect the natural history of the disease and its treatment in different ways. AIM: To summarise the available evidence regarding the influence of comorbidities on the natural history of multiple sclerosis. DEVELOPMENT: Patients with multiple sclerosis are at greater risk than the general population of developing both acute and chronic comorbidities. It has been shown that comorbidities can delay the diagnosis of multiple sclerosis after clinical onset, increase the rates of relapses and of accumulation of disability. Comorbidities also influence aspects of the choice of treatment and therapy adherence. Finally, comorbidities also increase the mortality rate and reduce the quality of life of patients with multiple sclerosis. CONCLUSIONS: Screening, diagnosis and treatment of comorbidities are a key aspect of caring for patients with multiple sclerosis to improve their long-term prognosis in terms of disability, quality of life and mortality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".