Predictive factors of depression in patients with cardiorespiratory failure
Bibliographic record
Abstract
Introduction: One of the most common comorbidities to the COPD is the heart failure, the presence of both is called Cardiorespiratory Failure. In patients with this condition, the presence of depression is common, and this can lead to complications in the treatment such in the patient’s quality of life, even raising the mortality rate. Nevertheless, there are few researches that explore the factors that predict this comorbidity. Aim: To research the predictive factors of depression in patients with cardiorespiratory failure. Methods: A descriptive cross-sectional study was conducted, involving 83 patients with cardiorespiratory failure. The following test was used: SF-12 Questionnaire for evaluating the quality life, the Hospital Anxiety and Depression Scale (HADS), the Montreal Cognitive Assessment (MoCA) for evaluated the cognitive impairment, the Psychological Well-being Scale. A multiple regression model analysis was performed by means of the SPSS software version 25. Results: The age’s average was 67±14 years old, 55.42% were men. The regression model (F (3, 51) = 51.20, p < .001, R2 = .62) that includes: psychological well-being (t= -6.3, p= .000, β= -.441), cognitive impairment (t= -3.7, p= .000, β = -2.45), and anxiety (t= 5.8, p= .000, β = .414), they explain 62% of the total variance of the model, over the others variables clinic and psychological adjusted for age and sex. Conclusions: The present research shows that the psychological well-being, the cognitive impairment and the anxiety influence in the depression prevalence in these patients, beyond medical factors. Hence is important a multidisciplinary treatment focused on the management of these factors.
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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 | 0.002 |
| 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 | 0.001 |
| 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".