Yoğun Bakımda Egzersiz Kapasitesini Etkileyen Faktörlerin Belirlenmesi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are many factors that affect exercise capacity in patients admitted to the intensive care unit (ICU). The aim of the study was to determine the factors affecting exercise capacity in intensive care patients. Thirty patients hospitalized in the ICU were included in the study. Charlson Comorbidity Index was used for comorbidity assessment. APACHE II and SOFA scores were used to evaluate the risk of mortality. The Glaskow Coma Scale was used for the level of consciousness. Peripheral muscle strength was evaluated by the Medical Research Council scale. Physical Function ICU Test (scored) was used for exercise capacity assessment. Barthel Index was used for functional level. Nottingham Health Profile was used to evaluate the quality of life. Cognitive status was evaluated with Montreal Cognitive Assessment Test. Hospital Anxiety and Depression score was used to evaluate anxiety and depression. Fried Fragility Index was used in the fragility assessment. Exercise capacity was found to be highly correlated with muscle strength (r = 0.817) and functional level (r = 0.861), low level with quality of life (r = -0.422) and moderately with cognitive status (r = 0.539) (p<0.05). It was found that muscle strength, functional level, quality of life and cognitive status had a significant effect on exercise capacity (p<0.05). As a result, it has been shown that muscle strength, functional level, quality of life and cognitive status affect the exercise capacity in patients admitted to ICU. Considering that it affects exercise capacity in ICU patients, muscle strength, functional level, quality of life, and cognitive status should be among the evaluation parameters when planning physiotherapy programs.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it