Fatores associados a fragilidade em pacientes com doenças neurodegenerativas
Bibliographic record
Abstract
PURPOSE: To identify the factors associated with frailty in patients with neurodegenerative diseases. METHODS: Cross-sectional study, whose sample consisted of 150 patients diagnosed with neurodegenerative diseases seen at a speech-language therapy clinic in a reference hospital in southern Brazil. A secondary exploratory analysis of the medical records of patients treated at this clinic between April 2016 and May 2019 was performed. The information collected was sex, age, education, type of neurodegenerative disease, time of disease, frailty (Edmonton Frail Scale-EFS), swallowing (Northwestern Dysphagia Patient CheckSheet-NDPCS, Eating Assessment Tool-EAT 10), and cognition (Mini-Mental State Examination-MMSE and Montreal Cognitive Assessment-MoCA). Continuous quantitative variables were analyzed using mean and standard deviation and categorical quantitative variables from absolute and relative frequency, as well as their association with the outcome using the Chi-square test. Crude and adjusted Prevalence Ratios were assessed using Poisson regression with robust variance. All statistical tests were considered significant at a level of 5%. RESULTS: The significant factors associated with frailty were the presence of oropharyngeal dysphagia and altered cognitive performance. Individuals with frailty have a higher prevalence of oropharyngeal dysphagia (PR= 1.772(1.094-2.872)), while cognition alteration presented a lower prevalence (PR= 0.335(0.128-0.873). CONCLUSION: Oropharyngeal dysphagia can be an important clinical predictive factor for consideration in cases of frailty in patients with neurodegenerative diseases.
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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 | 0.000 | 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) | 0.002 | 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".