2014 Joint ACTRIMS-ECTRIMS Meeting (MSBoston 2014) MS Journal Online: Poster Session 1
Bibliographic record
Abstract
Background: According to previous studies, 25% of caregivers of chronically ill patients have been reported to suffer some degree of burden.Latin caregivers experience caregiving as less challenging than Caucasians.Factors influencing burden include scholarship of the caregiver, relationship with the patient, motor and cognitive decline of the disease.To date there are no studies addressing the issue in Mexican caregivers of multiple sclerosis patients.Objectives: To describe the prevalence and predicting factors of caregiver burden in multiple sclerosis within a Mexican population.Methods: 99 Caregiver-patient pairs were recruited during scheduled office visits.Demographic data was recollected.Zarit Burden Interview (ZBI) was administered to caregivers and Expanded Disability Status Scale (EDSS) was calculated in each patient.Results: Some degree of caregiver burden was found in 60.6%, being moderate or severe in 31.1%.The "Impact of caregiving" subdimension accounted for most weight of the construct.The median patient disability according to the EDSS was 5.5.Variables predicting caregiver burden were EDSS (p < 0.001, CI 95% 0.09-0.21),daily hours of caregiving (p < 0.001, CI 95% 0.81-0.91)and incomplete high-school for caregivers (p = 0.030, CI 95% 0.01-0.79).Daily hours of caregiving also predicted each subdimension of the ZBI independently.Kolmogorov-Smirnov test was used to determine the goodness of fit of the normal distribution of the interval variables.A likelihood ratio test was performed comparing the complete model with a reduced one including only the predicting factors, obtaining a significant p = 0.0014 for 6 degrees of freedom.Conclusions: Caregiver's burden prevalence was much higher than reported previously.In fact, even moderate or severe burden were higher than the overall burden described in other studies.Predicting factors were similar to those found in previous reports.Considering the higher prevalence of caregiver burden found, it is possible that the study sample was underestimated.If this is so, there's a chance that two more variables (Age of caregiver and Months spent as caregiver) could have reached significance providing the sample was enlarged.According to the results, neurologists should implement supporting measures for caregivers, addressing the time they spend with their patients.Future research should focus in other patient's variables and cultural environment.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.183 | 0.060 |
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".