Yoğun Bakımda Egzersiz Kapasitesini Etkileyen Faktörlerin Belirlenmesi
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".