Understanding the Independent Predictors of Dysphagia-Related Quality of Life in Stroke Survivors
Bibliographic record
Abstract
PURPOSE: It is important to pinpoint modifiable factors contributing to reduced dysphagia-related quality of life (QoL) in order to improve treatment outcomes and patient health given that a large proportion of stroke survivors experience dysphagia. The purpose of this exploratory study was to identify the independent predictors of dysphagia-related QoL in community-dwelling stroke survivors. METHOD: = 57 years; seven males) participated in the study. Survivors were > 3 months poststroke and living with their partner. Backward regression analysis methods were employed to determine independent predictors of dysphagia-related QoL using scores from the Swallowing-Related Quality of Life questionnaire. Independent variables tested included age, employment status, receiving dysphagia treatment, number of medical conditions, level of diet modification, Stroke Impact Scale (SIS) scores, relationship with partner, partner age, partner employment status, partner burden, and partner depression. RESULTS: < .001). More specifically, stroke survivors with more medical conditions or a partner who worked outside of the home had worse dysphagia-related QoL, and those with better mental health or a less modified diet had better dysphagia-related QoL. CONCLUSION: Factors related to dysphagia-related QoL are multifactorial and include both survivor and spousal variables. The results of this exploratory study highlight the need for clinicians and researchers to consider patient function and needs more wholistically to maximize perceived QoL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".