Prevalence of pain and fatigue in post-stroke patients: a study transverse.
Bibliographic record
Abstract
Background: Motor and functional recovery in post-stroke individuals is a process of interference from non-motor aspects such as pain and fatigue.The prevalence of these symptoms and their impact on the rehabilitation process need to bebetter understood and studied, mainly in the strategies for the construction of therapeutic approaches. Objectives: To analyze the prevalence of pain and fatigue in individual’s post-stroke. Design and setting: Cross-sectional study that followed the STROBE recommendations. An outpatient clinic at UFRN / FACISA in Santa Cruz-RN institution. Approved by the Research Ethics Committee (Opinion No. 2,622,853). Methods: One sample perconvenience, had 29 post-stroke individuals. The individuals were evaluated using the following clinical instruments: the Mini Mental State Examination (MMSE),Functional Independence (FI), Fulg-Meyer Scale (FMS), Sensory Assessment of Nottingham (SAN), McGill Pain Question (MOQ) and the Severity of Fatigue (SSF). The data were analyzed descriptively. Results: Participants presented the following characteristics clinical: MMSE, 19 (median), (0(1ºQ)/24 (3ºQ)); FI, 80 (0/121); FMS, 36 (36/88); SAN, 108 (0/108); SSF, 9 (0/27). Thus, the prevalence of pain was 28% (8) and the fatigue was present in 76% (22) of the individuals. Conclusions: We found a high prevalence of fatigue in patients with chronic stroke, however we did not find relationship with pain. Further studies are needed to understand these conditions and identify which factors contribute to the prevalence of such symptoms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".