Predictive factors of erectile dysfunction in a cardiac rehabilitation programme. What can we improve?
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Introduction Erectile dysfunction (ED) is a frequent comorbidity in patients with chronic coronary syndrome; it is related to both cardiovascular risk factors (CVRF), the treatments administered and the vascular disease itself. We analyse the predictors of ED in the cardiac rehabilitation unit of a third level hospital. Material and methods Observational and prospective cohort of male patients participating in the cardiac rehabilitation programme (CRP) between 2015 and 2019. This CRP lasts 1 month, with 3 weekly sessions of monitored and supervised exercise, in addition to talks related on the cardiovascular area, diet, etc. One of these sessions is on ED, in which the patient is asked about the entity, possible interactions with drugs, etc. In addition, the degree of ED is evaluated anonymously using the IIEF5 scale, in which 5 questions are used to evaluate different conditions of the disease by scoring from 1 to 5, with values of less than 21 being considered as ED. For the statistical analysis we carried out a multiple regression model adjusted by the clinical variables collected at the beginning of the programme. Results Out of a total of 343 patients 90% were male and 265 completed the questionnaire. 63% presented ED. The mean age is 56 ± 9 years. The distribution of CVRF: 43% hypertense, 23% diabetics, 72% dyslipemia, 22% ex and 48% smokers. As for the diagnoses prior to the start of CRP, 59% suffered from STEMI, 22% from NSTEMI, 9% from unstable angina and 9% were post-operative from cardiac surgery. In the global multiple regression model the factors predisposing to ED and presenting statistically significant coefficients were age (-0.2 CI: -0.27- -0.12, p < 0.001), hypertension (-1.6; CI -2.9- -0.3, p = 0.014) and diabetes (-2.0 CI: -3.1- -0.6, p = 0.007). We performed a predictive model for the different age ranges according to the presence/absence of hypertension and diabetes (Figure 1). Conclusions ED is a prevalent entity in our series, with age-adjusted multivariate analysis predicting its development and hypertension and diabetes as potentially modifiable factors related to it. Abstract Figure. Graph 1: Predictive regression model
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".